MGMT3120 - Construction Quality Management Group Project 2: Developing a Construction Quality Control (CQC) Plan Resources Needed: Project Drawings & Specifications Construction Quality Control (CQC) Plan Structure CQC Plan Template Sample CQC Plan Inspection and Testing Plan (ITP) Template ITP Example Submittal Log Template Submittal Log Sample Instructor: Professor Bill Nichols Project Mark: 20% of the overall course grade Submission Due: Week 14 (To be submitted via Brightspace. Refer to specific requirements below.) SUBMISSION REQUIREMENTS Submission Deadline: Week 14 (11:59pm, Sunday, April 13) Upload your files in the Group Projects/Project 2 folder. Late Submission Policy: Any late work submitted after the deadline will incur a penalty of 10% reduction per day for up to 5 academic days for a maximum of 50% reduction, after which no work will be accepted. Marks are allocated on the basis of the soundness of your response, and the quality of documentation/reporting. Where assumptions are made, you must provide a clear description of the same. General Instructions: 1. This project is to be completed in groups of no more than 4 students. The inclusion of any additional student to a team beyond the 4 persons will incur an automatic penalty of 25% for all team members. Each member of the team is required to contribute equally. Marks will be allocated based upon the share of students’ overall participation. Teams are strictly prohibited from copying from one another, although collaboration is encouraged. Any evidence of plagiarized work will be assigned a mark of 0 for all members of the team. 2. Students are responsible for placing themselves into groups. 3. Assume the role of the project manager for the general contracting firm who has recently been awarded the project under the design/bid/build (CCDC 2 & CCA 1) project delivery method. 4. As part of your contractual requirements, you are to develop a CQC Plan, Master Construction Plan with Submittal Milestones, ITP and Submittal Log documentation for the assigned project. This document is to be submitted to the Owner and/or the Consultant as per the contract. Assignment: Developing a Construction Quality Control (CQC) Plan The Project: York Region Paramedic Response Station #32 Project Duration: April 1, 2025 - August 31, 2026 Assignment Requirements: Part A - Development of Construction Quality Control Plan 1. Prepare a minimum of 25 Definable Features of Work (DFOW). You will have to break down the project into its various activities to establish Definable Features of Work (DFOW). This may require that you develop a work breakdown structure (WBS) to develop the work activities for all major divisions. You can use the subcontracting work packages developed in Group Project 1 for the definable features of work for your project. 2. Prepare relevant construction quality control procedures applicable to your project. Use the CQC Plan structure provided as your guide to develop these procedures. 3. Prepare various Construction Quality Control (CQC) forms to accompany the respective QC procedures. As a minimum, you will be required to prepare the QC forms as indicated in the CQC Plan structure provided. Use the typical forms included in the sample CQC Plan and customize them to your project. Forms and checklists are a critical component of any construction quality control plan. In the plan, you will include sample forms with the project-relevant information that will be used by QC personnel during the execution of the project. You are encouraged to use your creativity and imagination. 4. Prepare a complete Construction Quality Control (CQC) Plan using the exact structure as presented below. 1. Project Description 1.1 Site Description 1.2 Mission Statement of Quality 1.3 Definable Features of Work 2. Project Organization 2.1 Project Organization Structure 2.2 Quality Control Personnel Qualifications 2.3 Roles and Responsibilities of Quality Control Personnel 3. Construction Quality Control Procedures 3.1 Procedure for Quality Control Meetings & tracking forms 3.2 Procedures for project document control & tracking forms 3.3 Procedures for inspections and testing & tracking forms 3.4 Procedures for receiving material inspections & tracking forms 3.5 Procedures for equipment commissioning & forms 3.6 Procedure for stop work order & forms 3.7 Procedures for project completion and handover & tracking forms 4. Construction QC Documentation 4.1 Construction Production Reporting Procedures 4.2 Construction Quality Control Daily Report Forms 4.3 Construction Quality Control Weekly Report Forms 5. Construction QC Submittals 5.1 Submittals review procedure for shop drawings, material data, samples and product data 5.2 List the submittals 5.3 Submittals Tracking Form. 6. Management of Nonconformance 6.1 Nonconformance reporting procedures & tracking forms 6.2 Deficiency reporting procedures & tracking forms 6.3 Reworks reporting procedures & tracking forms 6.4 Punch list procedures and tracking forms 7. Corrective Actions 7.1 Procedure for Corrective Actions 7.2 Corrective Action Tracking Form. 8. Appendices Part B - Appendix A - Master Construction Schedule with Submittals Milestones 1. Using the master construction schedule developed in Group Project 1, identify a minimum of 10 most critical submittals (shop drawings, samples, as-builts, etc.) as project milestones. Show the submittals deadlines as milestones on your construction schedule. Part C - Inspection and Testing Plan (ITP) 1. Prepare an Inspection and Testing Plan (ITP) using the template provided. An example is provided to assist you in completing this documentation. You must include a minimum of 10 inspection and testing requirements for the specified definable features of work for your project. The Specifications document identifies the quality requirements and standard tests required for products and works. The timing of the identified inspections/tests has to correspond to the master construction schedule developed above. Part D - Submittal Log 1. Prepare a Submittal Log for the project using the template provided. An example is provided to assist you in completing this documentation. The Submittal Log must include a minimum of 10 submittal items. The submittals identified in the log must be pertinent to your project’s specific processes. Each part is to be assembled as a separate chapter. Grading Rubric: You team work will be graded using the rubric provided below. The marks will be awarded on the basis of accuracy, thoroughness, clarity, and overall quality of the completed assignment having regard to report organization (format, page numbering, grammar, relevance to assigned project), writeup of construction quality control procedures, and preparation of construction quality control documentation (forms & checklists) applicable to the project. Although group collaboration is encouraged, any evidence of cloned or copied work between the groups or copyright violations will earn a mark of zero and an appropriate action for academic dishonesty will be executed. Item Maximum Mark Part A - Construction Quality Control Plan (Procedures, forms and checklists) 65 Part B - Master Construction Schedule with Submittals Milestones 10 Part C - Completed ITP 15 Part D - Completed Submittal Log 10 Total 100
ETB1100 Business Statistics Tutorial 10 Driving Business Decisions Using Regression: Exploring Relationships and Predictive Power Through Correlation and Linear Regression Analysis Download files from the Tutorial TEN Questions folder on Moodle If you have not already done so, download the following files for use in these Week 10 Tutorial questions which are found in the Tutorial TEN Questions folder on Moodle . • Random Sample_DEMO.xlsx • Burgers.xlsx • Car Dealership.xlsx Q10.1 This question uses data in the worksheet labelled Burger, in the file labelled Burgers.xlsx It gives real data on the SALES and PRICE for franchises of a (unnamed) burger chain in a selection of different cities across the US. SALES is in thousands of dollars and is the dependent variable (Y), while PRICE is an index over all products sold in a given month and is expressed as a notional number of dollars for a meal, and is the independent variable (X). (a) What do you expect the relationship to be between PRICE and SALES? (b) Use Excel to plot a scatter diagram [Insert>Scatter and select unjoined dots] of SALES(Y) against PRICE(X), (with SALES on the vertical axis). Remember to follow the approach that you practiced in your Pre-Class Exercises on the ice cream sales data and display the R2 on each chart. Comment on how this visual relationship compares with your expectations. (c) Estimate a model for the relationship between SALES and PRICE, by using Excel to produce the simple linear regression output. Remember to follow the approach that you practiced in your Pre-Class Exercises on the ice cream sales data. Include the following extra feature: In addition to checking “Labels”, also check “Confidence level” and in the adjacent field, type “99” (This will provide a 99% confidence interval for population coefficients in addition to the 95% confidence interval that is always provided.) As a check that your output is correct, make sure that that the fourth number from the top of the output, Standard Error, is 5.096858 (d) Based on the output produced in (c), state the estimated linear regression equation for this data, being sure to define the variables. (e) Using a 5% level of significance, conduct a hypothesis test to determine whether there is evidence that a linear relationship exists between PRICE and SALES. (Remember, only the p-value approach is used in regression analysis). Remember to show ALL working, ALL steps AND interpret the conclusion in context of the question. (f) What is the slope of the estimated regression line? Provide an interpretation of this value. (g) State the value of the intercept of the regression line. Give an interpretation of this value and discuss whether it is meaningful in this case. [Note that when interpreting the intercept and slope, it is important to take account of the units in which the data is specified. In the current case, in particular, the sales level is in thousands of dollars.] Please have this output ready to discuss in the tutorial. (h) State the sample value of the correlation coefficient between PRICE and SALES, and interpret this value. (i) State the coefficient of determination and interpret this value. (j) Using your estimated regression equation, predict the average/expected sales amount a franchise could expect if the cost of a meal was set to $6.25. (k) Is this prediction likely to be reasonable/valid? Explain briefly. (l) State and interpret the 95% and 99% confidence intervals for the slope coefficient. Compare and comment on the width of these intervals. Q10.2 This question uses the data file Car Dealership.xlsx A used car dealership is considering the factors that determine the sale price of used Toyota Camry passenger vehicles. As a first attempt at predicting the price, it is assumed that the main factor affecting the resale value is the distance the car has travelled (i.e. the odometer reading). For a sample of 13 cars, the following data is obtained: (a) What do you expect the relationship to be between Price and Odometer Reading? (b) Use Excel to plot a scatter diagram against Odometer Reading vs Price. Remember to follow the approach that you practiced in your Pre-Class Exercises on the ice cream sales data and display the R2 on each chart. Comment on how this visual relationship compares with your expectations. (c) Based on the scatter plot, comment on whether it is appropriate to fit a regression line to the data. A regression analysis was performed using Excel, with the following result: (Since you have the data, you should also produce this regression output and check your output against that which is provided here.) (d) Using the Excel output provided, state the estimated linear regression equation for this data, being sure to define the variables. (e) Using a 5% level of significance, conduct a hypothesis test to determine if a linear relationship exists between ODOMETER READING and SALE PRICE. (Remember, only the p-value approach is used in regression analysis). Remember also to show ALL working, ALL steps AND interpret the conclusion in context of the question. Given that a linear relationship DOES NOT exist, in the workplace, there is no point in continuing with this model, interpreting the slope coefficient etc. HOWEVER, IN THE CLASSROOM, WE WILL TAKE ADVANTAGE OF THIS EXAMPLE AND STILL USE IT FOR SOME EXTRA PRACTICE HERE . (f) What is the slope of the estimated regression line? Provide an interpretation of this value. (g) State the value of the intercept of the regression line? Give an interpretation of this value and discuss whether it is meaningful in this case. (h) Look again at the output. The first number at the top of the output is labelled “Multiple R” . This number is the absolute value of the sample correlation coefficient r between the two variables. In order to find the sign of the correlation coefficient, you must look at the sign of the slope. So, state AND interpret the value of the correlation coefficient between odometer reading and price? (i) State the coefficient of determination and interpret this value.
ST3370_B BAYESIAN FORECASTING AND INTERVENTION Summer 2022 Question 1 We consider the general objective of inferring an unknown parameter θ in a set θ from observations y1:n = (y1,...,yn) on R. We model the observations as realizations of y1:n = (y1,..., yn) whose law is defined by where µ(0), µ(1), σ2, π(0) and π(1) are known parameters and N(·; µ, σ2) denotes the density of a Gaussian distribution with mean µ and variance σ2 > 0. (a) Which conditions should π(0) and π(1) satisfy for pθ to be a density? [1 mark] (b) Let pθ / c(0)N(θ; µ(0), σ2) + c(1)N(θ; µ(1), σ2) with c(0), c(1) > 0 not depending on θ. What is the expression of pθ(θ)? [1 mark] (c) What is the prior mean of θ? To what extent, or for which range of parameters, would you argue that it is a good summary for the prior? If needed, propose an alternative and explain your choice. [3 marks] (d) For each j = 0, 1 consider the model (i) What is the posterior distribution for θ(j) given that y( 1: j n ) = y1:n? [3 marks] (ii) Based on the answer to Question 1(d)(i), find py(j)1:n(y1:n). [3 marks] (e) What is the posterior distribution for θ given that y1:n = y1:n? [Hint: use the answers to Question 1(b) and Question 1(d)] [3 marks] (f) With reference to conjugacy, (i) Given the expression of the prior for θ, which condition should the posterior satisfy to ensure that the prior is conjugate with respect to the likelihood? [1 mark] (ii) Based on the answer to Question 1(e) and Question 1(f)(i), if the prior for θ is conjugate with respect to the likelihood, comment on the update of all the parameters of the posterior and their behaviour when the number of observations n diverges. [3 marks] (g) Consider the following alternative model for the observations: where Ber(j; π)=(π)j (1−π)1−j {0,1}(j) denotes the density of a Bernoulli distribution with probability of success π. What is the posterior for θ' given that y' 1:n = y1:n? [2 marks] Question 2 You are given two observations (y0, y1) = (10, 9) at, respectively, time step 0 and time step 1. You are asked to model them through the following constant Gaussian DLM: (1) where θ0 = 10 + v0, vk ~ N(·; 0, 1) and uk ~ N(·; 0, 1) are all independent for k ≥ 0. (a) What are the hyperparameters of the DLM and their dimensionality? [1 marks] (b) Using the initial distribution for the state and the observations (y0, y1) = (10, 9), derive the explicit expression of the filtering and of the predictive distributions (both for the state and for the observations) recursively starting from the filtering at time 0 (p(θ0|y0 = 10)) until the predictive for the observations at time 2, (p(y2 | y0:1 = (10, 9)). [4 marks] (c) Provide two di↵erent ways of interpreting the relation between the filtering mean and the predictive mean for the parameter at the same time step. How does the variance change between the filtering and predictive distribution? Please provide an interpretation. [3 marks] (d) Which value for the state θ10 do you expect at time 10? [2 marks] (e) Consider a dynamic linear model of the form. where vk ~ N(·; 0, Vk) and uk ~ N(·; 0, Uk) are all independent for k ≥ 0. Let Cn,j,h = Cov(θn+j , θn+h | y0:n = y0:n) for j, h > 0. (i) Which assumptions are missing for the model to be correctly specified? [1 mark] (ii) Provide the expression of Cn,j,j in terms of the filtering distribution. [2 marks] (iii) For a fixed j > 0, provide a recursive algorithm to compute Cn,j,h for every h ≥ j. [3 marks] (iv) Let Dn,j,h = Cov(yn+j , yn+h | y0:n = y0:n) for j, h > 0. Provide a way to compute Dn,j,h for h>j. [2 marks] (v) Compare Cn,j,h and Dn,j,h for the model in (1) when h>j and compare them. What changes in the comparison when h = j? [2 marks] Question 3 Consider a time series model with the following state-space equations (2) where (θ0,1, θ0,2) = m0 + u0, vk and uk are all independent for k ≥ 0 and have mean zero. (a) What is the transition matrix F of the time series in (2)? [1 mark] (b) Consider a reparameterization of the model in (2) with θ0 k = (θ' k,1, θ' k,2)=(θk,2, 3θk,1). (i) Is (yk, θ0 k)k≥0 a state space model? Justify your answer and, if this is the case, highlight the expression of the transition matrix F0 . [2 marks] (ii) Is θ'k a linear transformation of θk = (θk,1, θk,1)? If so, express the linear transformation in matrix form. [1 mark] (iii) Use your answers to Question 3(b)(i) and Question 3(b)(ii) to find a canonical similar model to (2). [2 marks] (iv) Find the eigenvalues of F and confirm your findings. [1 mark] (c) You are asked to predict the position of a train and for five consecutive time intervals you observe it at y0:4 = (5, 5.1, 4.9, 4.7, 5.3) kilometers from Coventry station. Would you use the model in (2)? Justify its use or propose an alternative with reference to the findings in Question 3(b). [3 marks] (d) Starting from (2) consider a different space equation for y0 k that satisfies (3) where µ0 = b0 + w0, wk are independent for k ≥ 0, have mean zero, and are independent from (uk)k≥0 and (vk)k≥0. (i) How would you describe qualitatively the observations (yk)k≥0 arising from this model? Propose a phenomenon that could be modelled by (3). [2 marks] (ii) Is the state-space model in (3) observable? Justify your answer. [2 marks] (e) Can the superposition of two state-space models be observable? Justify your answer by providing an example. [2 marks] (f) Consider a Gaussian dynamic linear model M that is similar to model (2) and has observability matrix and ω ≠ 2πq for every q ∈ N. (i) What is a canonical model similar to M? [1 mark] (ii) Let Uk and Vk denote the variances of the transition and observation noises of M. What is the canonical equivalent model ? [3 marks] Question 4 Consider the random walk with noise process characterized by the following equations (4) where vk ~ N(·; 0, V ) and uk ~ N(·; 0, U) are all independent for k 2 Z. (a) Is (yk)k2Z autoregressive? Does this imply that it is stationary? Justify your answer. [2 marks] (b) Let ek = yk − yk−1. Is (ek)k2Z stationary? Justify your answers. [2 marks] (c) Show that the autocovariance of (ek)k2Z coincides with the one of a moving average model MA(p) for some p. How can we find the corresponding coefficients? [2 marks] (d) Consider the optimal Kalman gain Kk at time k of model (4). (i) Express Kk in terms of U, V and Kk−1 and use it to prove that Kk converges to a constant K as k → +∞. [Hint: use 0
ECET 35901 Computer Based Data Acquisition Applications Summer 2025 [Practical Assignment 6] Raspberry Pi GPIO in Node Red Objectives: • Configure the Raspberry Pi’s GPIO with Node Red. • Familiarize with the pinout of the Raspberry Pi 4 Model B. • Create a binary to seven segment decoder. Hardware Requirements: • Raspberry Pi 4 Model B (w/ Monitor, Keyboard & Mouse) • Breadboard • 4 Single Pole Double Throw (SPDT) Switches • Breadboard Wires • Common Cathode Seven Segment Display (Common Anode version is also possible) • 330 Ω Resistors [GPIO & Seven Segment Display] 1. Raspberry Pi GPIO: Observe the Raspberry Pi 4 Model B Pinout below in fig. 1. There are several things that must be considered before accessing the GPIO. Note that certain pins have dual functions in addition to being a GPIO pin, for example, GPIO 10 can also be programmed to be the MOSI line in SPI communication. Also notice the locations of the power and ground pins and that there are two different supply levels: +5 V and +3.3 V. Even though there are +5V pins, the Raspberry Pi operates using the 3.3 V logic level! Don’t be confused with the GPIO numbers (0-27) and the Raspberry Pi pin numbers (1-40)!! Figure 1 Raspberry Pi 4 Model B Pinout and Pin interfaces We can configure these pins using Node-RED by utilizing the GPIO node library. Recall that the nodes are located on the lefthand side panel. Scroll down until you see the Raspberry Pi section, and you will see the following nodes as shown in fig. 2. Figure 2 Raspberry Pi nodes We will focus on the top two nodes, “rpi – gpio in” and “rpi – gpio out” ,which configure a pin as an input or output. Drag the “rpi – gpio out” node onto the workspace and double click the node. You should see a window shown in fig. 3. In this window you can select which of the GPIO pins you want to map to. Under “Type” you have an option to set the output as regular bit output (HIGH/LOW) or a PWM output. In this lab we will use the regular “Digital Output” option. Figure 3 Raspberry Pi GPIO output properties In later labs, to configure the pin as GPIO input modes, you must select an option for a pull-up or pull-down resistor or none. You will see the GPIO input properties shown in fig.4. Figure 4 Raspberry Pi GPIO Input properties 2. Seven Segment Display: Study your common cathode seven segment display’s pinout in its datasheet. The most common pinout is shown in fig. 5, but yours might be different. Also if your segment display is common anode type, the ground connection should be to Vcc. Figure 5 Common Cathode Seven Segment Display Pinout - this can be vary by each product Your task is to create a binary to seven segment decoder, where four switches will represent each binary bit counting up to 15 or “A” in hexadecimal. With these four bits, you must decode the resulting number to a seven segment display. For example, “0001” should decode to “1” on the seven segment display, and “1111” should decode to “F”. See table 1 for exact details. Table 1: Binary to Seven Segment Conversion Binary Number Seven Segment Display 0000 “0” 0001 “1” 0010 “2” 0011 “3” 0100 “4” 0101 “5” 0110 “6” 0111 “7” 1000 “8” 1001 “9” 1010 “A” 1011 “B” 1100 “C” 1101 “D” 1110 “E” 1111 “F” [Node-RED implementation] Observe the following fig. 6. You must obtain the inputs from the switches using the GPIO nodes, then convert them into flow variables to be called in the “Binary to SS Decoder” Function. The hardware schematic using a common cathode segment display is provided in Fig. 7. Figure 6 Node-RED implementation examples with Rasberry Pi GPIOs Figure 7 Hardware Schematic with a common cathod segment led From the fig.6, the “Binary to SS Decoder” function then outputs Boolean values to be sent to the output GPIO nodes. The code for “Binary to SS Decoder” is provided below for your reference (not for copy and paste). Study the code, and note the flow variable names, and make sure you match those names to the outputs of the set/switch nodes. The GPIO pins to be used as inputs and outputs you use are free for you to choose. Also, the code can be greatly simplified, again, the code is for your reference, feel free to edit the code. // Variable Initilization var bit0 = flow.get("bit0"); var bit1 = flow.get("bit1"); var bit2 = flow.get("bit2"); var bit3 = flow.get("bit3"); var seg_a = {payload: null}; var seg_b = {payload: null}; var seg_d = {payload: null}; var msg_d = {payload: null}; var seg_e = {payload: null}; var seg_f = {payload: null}; var seg_g = {payload: null}; // Display "1" if (bit0 == true && bit1 == false && bit2 == false && bit3 == false) { seg_a = {payload: false}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: false}; seg_e = {payload: false}; seg_f = {payload: false}; seg_g = {payload: false}; } // Display "2" else if (bit0 == false && bit1 == true && bit2 == false && bit3 == false) { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: false}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: false}; seg_g = {payload: true}; } // Display "3" else if (bit0 == true && bit1 == true && bit2 == false && bit3 == false) { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: false}; seg_f = {payload: false}; seg_g = {payload: true}; } // Display "4" else if (bit0 == false && bit1 == false && bit2 == true && bit3 == false) { seg_a = {payload: false}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: false}; seg_e = {payload: false}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "5" else if (bit0 == true && bit1 == false && bit2 == true && bit3 == false) { seg_a = {payload: true}; seg_b = {payload: false}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: false}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "6" else if (bit0 == false && bit1 == true && bit2 == true && bit3 == false) { seg_a = {payload: true}; seg_b = {payload: false}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "7" else if (bit0 == true && bit1 == true && bit2 == true && bit3 == false) { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: false}; seg_e = {payload: false}; seg_f = {payload: false}; seg_g = {payload: false}; } // Display "8" else if (bit0 == false && bit1 == false && bit2 == false && bit3 == true) { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "9" else if (bit0 == true && bit1 == false && bit2 == false && bit3 == true) { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: false}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "A" else if (bit0 == false && bit1 == true && bit2 == false && bit3 == true) { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: false}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "B" else if (bit0 == true && bit1 == true && bit2 == false && bit3 == true) { seg_a = {payload: false}; seg_b = {payload: false}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "C" else if (bit0 == false && bit1 == false && bit2 == true && bit3 == true) { seg_a = {payload: true}; seg_b = {payload: false}; seg_d = {payload: false}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: false}; } // Display "D" else if (bit0 == true && bit1 == false && bit2 == true && bit3 == true) { seg_a = {payload: false}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: false}; seg_g = {payload: true}; } // Display "E" else if (bit0 == false && bit1 == true && bit2 == true && bit3 == true) { seg_a = {payload: true}; seg_b = {payload: false}; seg_d = {payload: false}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "F" else if (bit0 == true && bit1 == true && bit2 == true && bit3 == true) { seg_a = {payload: true}; seg_b = {payload: false}; seg_d = {payload: false}; msg_d = {payload: false}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: true}; } // Display "0" else { seg_a = {payload: true}; seg_b = {payload: true}; seg_d = {payload: true}; msg_d = {payload: true}; seg_e = {payload: true}; seg_f = {payload: true}; seg_g = {payload: false}; } return [seg_a, seg_b, seg_d, msg_d, seg_e, seg_f, seg_g]; Note that the power and ground lines are from the Raspberry Pi's outputs; you do not need external power supplies for your circuit. Below (fig.8) segment display’s schematic and connections are for the common anode type. Observe the common anode connection – the connected resister values can vary, typically 200-500Ω. Figure 8 Common anode segment display connections and pinouts [Submission] Once you complete the flow, 1. Export it as a .JSON file and submit the file in the Brightspace assignment folder. 2. Make a short video (50sec ~1min) to demonstrate the functionality. Just provide the video link for me to check (do not upload the video to Brightspace directly, otherwise some penalty will apply).
BECO011 Economics for Business Program Diploma of Business Semester Semester 1, 2025 Credit points 6 Requisite Nil SUBJECT DESCRIPTION This subject introduces students to the basic concepts, theories and principles of economics, and requires students to apply these concepts to both real and hypothetical business situations. The subject provides students with the opportunity to understand the broad economic contexts in which businesses operate, and to analyse contemporary economic issues and events presented in the mainstream media. SUBJECT AND LANGUAGE LEARNING OUTCOMES (SLOs; LLOs) Upon successful completion of this subject, you are expected to be able to: SLO001 Explain the core principles of market competition and market failures, the characteristics of a variety of market structures and the main factors shaping the macroeconomic environment in which consumers and businesses operate SLO002| Analyse and predict the effects of government policies and other economic forces consumer and business behaviour, market outcomes and the macroeconomic environment SLO003| Interpret and critically evaluate economic commentary in the media SLO004| Communicate economic analysis as multimodal texts LLO002| Effectively evaluate academic texts LLO003| Give an effective academic presentation LLO004| Write effective academic texts CONTRIBUTION TO PROGRAM The Diploma of Business aims to develop your theoretical and practical knowledge of business, your analytical and technical skills and your attributes as a capable student of business and an ethical member of society. This subject is a core unit and provides a foundation for further studies in economics, business and finance through an introductory analysis of consumer and business behaviour in a range of market structures This subject will help you achieve the following Program Learning Outcomes (PLOs): A1. Apply evidence, creativity and critical reasoning to solve business problems B1. Communicate information clearly in a form appropriate for its audience Graduate attributes PLOs SLOs and LLOs A. Intellectual rigor and creative problem solving A1 | PLO001 SLO001 SLO002 B. Communication and collaboration B1 | PLO002 SLO003 SLO004
DATA9001 Fundamentals of Data Science - 2025 General Course Information Course Code : DATA9001 Year : 2025 Term : Term 2 Teaching Period : T2 Course Details & Outcomes Course Description This course provides a broad overview of Data Science as a platform. for further studies in Data Science and an understanding and appreciation of Data Science in the modern world. Students will study the fundamentals of Data Science as it is applied in Computer Science, Economics, and Mathematics and Statistics. They will be introduced to topics such as databases, data analytics, data mining, Bayesian statistics, statistical software, econometrics, machine learning and business forecasting. The content of this course will be delivered via weekly live lectures with academics from three different Schools: The School of Mathematics and Statistics, the School of Economics and the School of Computer Science and Engineering. These concepts will be further explored through a series of tutorials/workshops. Course Aims The aim of the course is to provide a broad overview of probability theory, different statistical methods, regression analysis, and modern data science techniques. This course will provide a platform. for further studies in Data Science and Machine Learning. Course Learning Outcomes Course Learning Outcomes CLO1 : Apply probability rules in a given setting to calculate key quantities. CLO2 : Use key theoretical tools to explore the properties of random variables. CLO3 : Apply key methods of statistical inference in applied settings. CLO4 : Use R/RStudio to perform statistical computations and simulations. CLO5 : Apply various data visualisation tools, perform. regression analysis and draw causal inference from data. CLO6 : Apply fundamental data science techniques and tools, including machine learning, Naïve Bayes classification, Decision trees, K-NN, unsupervised learning and neural networks. Course Learning Outcomes Assessment Item CLO1 : Apply probability rules in a given setting to calculate key quantities. • Statistics Assignment • Final Exam CLO2 : Use key theoretical tools to explore the properties of random variables. • Statistics Assignment • Final Exam CLO3 : Apply key methods of statistical inference in applied settings. • Statistics Assignment • Final Exam CLO4 : Use R/RStudio to perform. statistical computations and simulations. • Statistics Assignment • Final Exam CLO5 : Apply various data visualisation tools, perform regression analysis and draw causal inference from data. • Economics Assignment • Final Exam CLO6 : Apply fundamental data science techniques and tools, including machine learning, Naïve Bayes classification, Decision trees, K-NN, unsupervised learning and neural networks. • Computer Science Assignment • Final Exam
Public Economics — Problem Set #2 Due: October 9thth at 2:40pm (submit through the courseworks) 1. Suppose that you have a job paying $2500 per month. With 10 percent probability, you may get sick and your monthly earnings will be reduced by $900. Assume that you spend all of your income on consumption and you have no savings. Your utility from consumption is given by u(C)=√ C and you are interested in maximizing the expected utility. (a) Suppose that you have no access to insurance. What is your expected income next month? What is your expected consumption? (b) What is the maximum price that you would be willing to pay for the first dollar of insurance? (c) Suppose that you can buy insurance that will cover all of your $900 loss. What is the maximum amount that you would be willing to pay for that insurance? (d) If you divide the amount obtained in part (c) by the amount of coverage purchased ($900), you should get a number that’s smaller than the price that you’d be willing to pay for the first dollar of coverage but larger than the actuarially fair price. Explain why. (e) Suppose the dollar of coverage costs q=1/9. How much coverage are you going to buy? 2. Consider a person with initial resources of $80,000 that is facing 20% probability of a loss of $60,000. The utility function in each state of the world is given by ln(C) where C is consumption (the derivative of ln(C) is C 1 ). The person maximizes expected utility. (a) What is the maximum price that the person is willing to pay for the first dollar of coverage? (b) Suppose that the price of dollar of coverage is equal to 1/3. How much insurance would this person buy? (c) The answer in the previous part does not involve buying full coverage. At what price would the full coverage be bought? Is it possible that this could be the market price? 3. Suppose that the government considers a new “social insurance” program: a payment to renters who got evicted by their landlords (it is an insurance against an evil landlord). Can you think of a justification for this policy? What are possible costs? What should we know to decide whether introducing this program makes sense? 4. Note: the objective of this problem is to take you through an example of how one might think about consequences and trade-offs involved in social insurance. Think about it as an extension of lectures rather than a problem to be solved. What needs to be done in the problem is mathematically relatively simple, the hard part is interpreting the solution. Suppose that a person has the utility function given by √C−D where C is consumption and D is the dis-utility of work or other effort. A person that is employed has the dis-utility of work given by D. A person that is unemployed and exerts effort of s to find a job experiences disutility of search given by s 2 . A person that works earns w. She has therefore the utility of √ w−D (we assume that all income is consumed). A person that loses a job searches for a new one and finds it with the probability of s. The utility of a jobless person is given by s(√w−D)+(1−s) √B−s 2 (1) B represents unemployment benefits, the first two terms represent the expected utility from outcomes following the search (with probability s the person finds a job right away, otherwise she receives unemployment benefits). (a) Find the optimal level of search s. What does it depend on? How does the presence of unemployment insurance affect search? Explain what is the moral hazard here and how the policy induces this behavior. Now, let’s denote this optimal level of search as s(B) to highlight that it depends on the level of benefits. The objective of the government is to maximize overall utility of the person. We need two more elements. First, let the probability of losing a job be denoted by 1−p. Thus, the expected utility of the person is given by p·[ √w−D]+(1−p)[s(B)(√ w−D)+(1−s(B))√B−s(B) 2 ]. Second, benefits have to be financed somehow. We will make a very simple assumption: the cost of a dollar spent by the government is given by some number γ measured in the same units as utility. The total amount of money that the government needs to spend to finance benefits is given by (1−p)(1−s)B (because (1−p)(1−s) is the probability that unemployment benefits will need to be paid out). The objective of the government is therefore to maximize p·[ √w−D]+(1−p)[s(B)(√w−D)+(1−s(B))√B−s(B) 2 ]−γ(1−p)(1−s(B))B. (2) with respect to the level of benefits B. (b) Can you explain using the above expression what are the benefits and costs of increasing unemployment insurance? Can you relate it to the discussion in class about the optimal social insurance? (c) What do you find unrealistic about this model? Are there examples of moral hazard that we assumed away but that may be important? (d) (Much harder and boring, only if you are very brave, there is no extra credit for it). See how much progress you can make in actually solving for the optimal level of benefits (do not expect though to get an explicit solution for B, but only to derive a condition that B needs satisfies). 5. Consider the case of unemployment insurance. (a) Both moral hazard and adverse selection may lead to problems with private provision of insurance products. Both of these are present in the unemployment insurance context. Give an example of each in the context of unemployment insurance and explain why they cause problems for private insurers. (b) Many governments implement public unemployment insurance programs. Are they immune to the problems you identified above? Explain. If they are still facing one or both of these problems, explain why it still may make sense for them to intervene. 6. Consider an individual that lives for at most two periods. The probability that she survives until period 2 is 0.8. Initial wealth is $10,000 and, if she is alive in period 2, she will then receive a Social Security check for $4,600 and no additional income. Suppose that the price of private annuity is actuarially fair, the interest rate is zero and that the person chooses to have the same level of consumption in the two periods by appropriately annuitizing (so that there is no saving from period one to period two, other than through the annuity that is purchased). What is that level of consumption?
Year 2 Integrated Business Functions Assignment 2 (20 marks) Module 4: Organizing for value creation This is a GROUP assignment, which is marked out of 100. It is worth 20 marks of the total course scores (i.e., 600 marks). The written report is due on 28 March 2025 (Week 30) by 5:00 pm. Word Limit: 2500 words (excluding appendices and footnotes) Read ALL the information in the exhibits ofthe business case PolarStar and attempt ALL FIVE questions. .ExhibitsCorrespondingQuestions1.Video (Rick Thompson &Sara Tho tionsQ1 – Q64.PolarStarAirways information (Vis 23Q2,Q3,Q56.Sara’s Diary 2 –April12, 2023Q2,Q3,Q57.Meeting Minutes (1) – HR iss es issuesQ2,Q49.Meeting Minutes (3) – Flight operations You should apply what you have learned in Module 4 and study all exhibits provided to develop your answers. In addition to conducting business analyses, you will need to provide sound and viable recommendations that are actionable and feasible. Ensure that both your analyses and recommendations are supported by evidence, clearly demonstrating strong organization and with good rationale. The guiding questions provided will help you structure your thinking and analysis as you prepare your answers. Keep in mind that these questions are not exhaustive, so feel free to explore additional questions or areas of inquiry that may emerge as you analyze the exhibits to develop your responses. 1. Analyze the impact of external environmental forces on PolarStar’s current business and future development (20%)? Guiding questions: • How do forces in the macro-environment affect PolarStar in terms of customer demand and preferences as well as company decisions and operations? • How do competitors affect PolarStar’s business? • How do infrastructure developments or limitations impact PolarStar's growth potential? • In what ways do environmentally friendly issues pose challenges or opportunities for PolarStar, and how do they affect the company's sustainability initiatives? 2. Identify and discuss major issues in the management functions of PolarStar that CEO Sara Thompson needs to tackle. Please state at least TWO major issues in each management function (20%). Guiding questions: • What are the key management issues faced by PolarStar with respect to its a) Planning, b) Organizing, c) Leading, and d) Controlling functions? You should identify at least TWO for each function. • How do the identified management issues impact PolarStar's ability to achieve its vision, mission, and goals? 3. Please make recommendations to address the management function issues of Planning that you identified in Q2. Specifically, derive THREE major goals to be attained, providing justification with reasons and a timeline for attainment (15%). Guiding questions: • How do your goals align with PolarStar's overall business goals, vision, and mission? • What evidence from the exhibits supports the need for establishing these specific goals? • What is the proposed realistic timeline for achieving each goal? • How will achieving these goals positively impact key stakeholders (employees, customers, investors, etc.)? 4. Please make recommendations to address the management function issues of Organizing that you identified in Q2. You should also consider the goals established in Q3 (15%). Guiding questions: • How should the organizational structure of PolarStar be redesigned to address the management function issues of organizing and achieve its goals? • What are the necessary roles and responsibilities of employees in PolarStar? • How should PolarStar prepare its human resources for achieving the goals through staff development and selection? 5. Please make recommendations to address the management function issues of Leading that you have identified in Q2 (15%). Guiding questions: • What significant challenges might the CEO Ms. Sara Thompson encounter when implementing the recommendations from Q3 and Q4, and how can these challenges impact the organization's progress? • What potential leadership-related challenges might CEO Ms. Sara Thompson face when implementing these recommendations, and how could these challenges affect overall organizational morale and performance? • Which leadership theories (e.g., trait, behavioural, contingency, transactional, transformational or servant leadership, etc.) should the CEO apply to effectively address these challenges and ensure successful implementation of the recommendations? • What specific leadership styles and skills should the CEO use to enhance employee motivation and engagement beyond traditional leadership approaches? • What other policies and measures should be taken to motivate the employees of PolarStar? 6. Please make recommendations to address the management function issues of Controlling that you identified in Q2. Recommend three control mechanisms to be implemented for monitoring progress and ensuring the attainment of the goals within the proposed timeline stated in Q3, with elaborations (15%). Guiding questions: • What specific tools and metrics can be used to effectively measure and evaluate organizational performance at PolarStar? • Which performance evaluation methods can be utilized to effectively assess and promote employee productivity and effectiveness? • What contingency plans should be developed to ensure that PolarStar can quickly adapt and maintain operational continuity during unforeseen challenges or situations? • What specific managerial actions can be taken to enhance the effectiveness of the controlling process and address any identified performance gaps? INSTRUCTIONS ABOUT THE GROUP WRITTEN REPORT 1. Submission due date: 28 March 2025 (Friday) by 5:00 pm No extension of the submission due dates is allowed. 2. Assignment Submission As a mechanism to maintain academic integrity, students are required to submit the soft copy of their assignments as below: i. Submission of soft copy Students should upload a soft copy of the assignment to the OLE of the course by 5:00pm on the submission due date. Files uploaded to the OLE should be prepared in Microsoft Word. Please refer to the quick start guide for submission of assignments to Turnitin. You do not have to submit a hard copy of your assignment. ii. 10% of the marks awarded to the assignment will be deducted for each calendar day overdue until the soft copy of the assignment is submitted. 3. A maximum length of this group written report is 2500 words (excluding appendices and footnotes). Please state the word count on the cover page of your written report. A template of the cover page is provided at the end of this assignment. Failure to comply with the above may result in mark deduction based on the followings: ords or less5 marksii.exceeding word limit by 251 and up t ord count on thecover page**5 marks
FIT9137 Assignment 3 1. Test the commands in terminal before writing into config file 2. Remember to save file 3. Remember to take snapshot Task A: Routing Requirements: • All hosts can be connected to each other • Add default route for all routers • Choose optimal path for Talos network ❖ Minimal propagation delay (i.e. least hop count) ❖ Largest Link bandwidth (decided by the slowest link speed) Network command: • Add routing table entry------------ ip route add 10.0.1.0/24 via 10.2.0.1 • Add routing table default entry------------ ip route add default via 10.2.0.1 1. R4 connect 93.69.53.0/24 via R1 R4 connect 93.69.26.0/24 via R2 R4 default via R2 2. R1 connect 93.69.32.0/24 via R4 R1 connect 93.69.26.0/24 via R4 --->R2 (higher bandwidth) R1 default via R4 3. R2 connect 93.69.32.0/24 via R4 R2 connect 93.69.53.0/24 via R4--->R1 R2 default via R3 4. R3 connect 93.69.53.0/24 via R2---->R4---->R1 (higher bandwidth) R3 connect 93.69.26.0/24 via R2 R3 connect 93.69.32.0/24 via R2 R3 default via internet 5. Minerva default via internet 6. Internet connect 93.69.0.0/16 via R3 Internet connect 148.130.0.0/16 via Minerva In the report, you need to include: • Routing path selected for each router and explain reason • Configuration in each Router • Screen shot of your network connectivity testing (i.e. successful ping test or tcpdump) Task A: Debugging After using Ping command, the potential error message • Destination net unreachable • Destination host unreachable • No response Task B: DHCP Server Requirements: • All Clients device (except leto) in Delos network acquire IP address ,subnet mask, DNS server and default gateway dynamically Configuration steps: • Complete DHCP setting in Minerva (refer to R1 DHCP setting) • Enable client side DHCP by removing static IP In the report, you need to include: • Configuration in Minerva • Configuration in client device • Screen shot of • Client device can obtain IP address within specified IP range • DHCP message in network (command: dhclient)
SOLA 5053: Assignment 1 2025 The Wind Resource DUE: by 17th March 5pm Out of 75 (includes quality mark worth 5 marks – 70+5) worth 15% Submit via Moodle. All work will go through Turnitin. Understanding the wind resource Weather and climate [12 marks] Question 1 (a) Figure 1a shows the location of the wind farm sites all the wind farms in the South and SE States. Figure 1b is the associated MSLP chart, which corresponds to around Monday 10th February 2025 (a snapshot at 11am). By looking at the synoptic map (Figure 1b), name all the features marked with blue arrows (4 in total), and in a short sentence explain their effect on the weather. [4 marks] Figure 1a. Location of the wind farms in the SE and Eastern states. Figure 1b. MSLP chart associated with Fig. 1a. (Hint: You may wish to refer to the following webpage on the Australian Bureau of Meteorology website which describes the motion of air in high and low pressure systems with animations, and provides a basic introduction to weather maps and isobars:www.bom.gov.au) (b) The wind farm energy production by capacity is shown in Figure 1d for Tasmania, and one wind farm in Victoria. As you can see it is highly varied depending on location and weather. Looking at all Figures 1a. 1b and 1c, 1d, note down how the weather system will impact the wind farms in Tasmania, relate it back to the capacity data. What could have made Bald Hills wind farm so high? (Note you may need to go onto the website https://anero.id/energy/wind-energyto see the names of the farms) [4 marks] Figure 1c, zoomed in of 1a Figure 1d. Capacity factor of wind generation from Tasmania and Bald Hill wind farm Vic. (c) Looking at Figure 1a. Note down on a state wide level (NSW, VIC, SA and Tas) how each state performs overall in relation to the weather map (weather systems each state is experiencing) in Figure 1b. What can we conclude about grouping of wind farms based on your findings? [4 marks] https://anero.id/energy/wind-energy Weather maps [12 marks] Question 2 Figure 2 and 3. MSLP chart for Winter and Summer, retrieved from the BoM http://www.bom.gov.au/australia/charts/synoptic_col.shtml. a) Produce a table stating the relative wind direction (using the meteorological convention) for each location, for each chart. Format your table in the same way as Table 1. Note (for relative wind speed strength choose between calm-gentle; moderate; strong)[9 marks] Location 1 . Day Wind Direction Relative Wind Speed Strength 14 July 13 November Location 2 Day Wind Direction 14 July 13 November Location 3 Day Wind Direction 14 July 13 November b) Discuss how the wind power production with the evolution of the weather pattern in winter (Fig. 2 - July) would change over the 3 locations. Refer to the current situation, and knowing how the pressure systems move from west to east explain how the States locations power production would change as the systems move across. [3 marks] The atmosphere [16 marks] Question 3 The following information is for the questions below: Location 1 – near Perth Airport WA Annual average Surface temperature – 18.8 C Annual average Mean sea-level pressure (MSLP) – 1015 hPa Elevation – 20 m above sea-level Surface profile – forest land Scale height – 8000 m Atmosphere – dry Location 2 – near Mudgee, NSW Annual average Surface temperature – 15.5 C Annual average Mean sea-level pressure (MSLP) – 1019 hPa Elevation – 471 m above sea-level Surface profile – farmlands isolated trees and small buildings Scale height – 8500 m Atmosphere – wet R= 286.9 [J/kg K] Turbines with a hub height of 100 metres exist at locations 1 and 2. [IMPORTANT NOTE: MSLP at a location refers to what the surface pressure would be at that location if it were at sea-level, that is 0 m elevation. The actual pressure at the ground level will be different if it is elevated.] For BOTH locations: [NOTE: marks are total for both locations, under each answer first calculate Location 1 and then Location 2 – see Figure 5] (a) Calculate the density of the air at hub height. [6 marks] (b) Estimate the gradient wind speed using the isobar spacing and the equation for geostrophic balance and the data provided for the gradient height in Figure 5. Clearly mark on the map the Δx selected to evaluate ΔP. [6 marks] Figure 5. Locations for question 3, part b. Retrieved from BoM Table 2. Gradient height data [1] (c) Based on your answer to part b) and using the data provided for the roughness length scales in Table 2, estimate the wind speed at hub height. When selecting a roughness scale, pick a mid-point value. [4 marks] Characterising the wind resource [20 marks] Question 4 a) Consider the data in the excel spreadsheet which represents recordings of wind speed taken at the Mudgee BoM station at 10m above ground level in km/hr. Using the hourly values determine the factors 'c' and 'k' for the year 2015. Show all relevant calculations (you can use excel/python to sort the data, and remember the units) . [3 marks] b) In the same excel spreadsheet there is also data for the same latitude and longitude as the Mudgee station, but from MERRA-2 reanalysis data (100m height) for the year 2015. Using the hourly values determine the factors 'c' and 'k' for the year 2015. Show all relevant calculations. [2 marks] c) How do the values for ‘c’ and ‘k’ compare from the two different datasets? Comment on what could be a cause of any differences in values. [3 marks] d) The ``c'' and ``k'' parameters that you calculated in Q4-a were obtained from a bureau of meteorology (BoM) weather station closest to Location 2 (from Figure 5). The data was recorded at 10 metres elevation (above ground level) and the nearby terrain is farmland, with few trees and buildings. You are considering expanding the Crudine Ridge Wind Farm (134 MW). The area you propose is predominantly open grassland. The turbines you wish to install will have a hub-height of 100 metres. (i) Determine the “c” and “k” parameters that are relevant for your prospective wind farm at the wind farm site (using the BoM data). Show all working (explain all working and include a print screen of the excel/python sheet used to determine the factors). [4 marks] (ii) Translate the “c” parameter at hub height (100m) at the BoM station location, and calculate the ‘’c’’ and ‘’k’’ values for the MERRA2 data tab Crudine Ridge Wind farm. Using the three values of “c”(station, station translated wind farm and MERRA2 Crudine Ridge Wind farm), calculate the power potentials (wind power per unit area) at: • Hub height at the station location • Hub height at the wind farm location • Hub height using the MERRA data in the tab Crudine Ridge wind farm [4 marks] e) Comment on the implications of any differences for predicting the wind power potential. Give two reasons why wind data from a weather station should not be directly used when predicting the wind resource for a nearby wind farm (justify your reasoning). [3 marks] f) How does the MERRA2 data at the site (tab named MERRA2 Crudine Ridge Wind farm) compare to your translated wind farm potential data? [1 mark] Wind farm performance [10 marks] Question 5 The following questions will use the Weibull parameters calculated in questions Q4-d (for the wind farm location at hub height and the MERRA2 wind farm location Crudine Ridge wind farm) and the wind turbine power curve in Table 4. (a) Present the following graphs for both sets of Weibull parameters: (i) Weibull PDF (ii) Weibull CDF Present results of both sets of Weibull parameters on a single set of axes and comment on the differences you see between the two values used. [3 marks] For both sets of Weibull parameters for the following questions: (b) (i) Present the velocity-duration curve (present results of both sets of Weibull parameters on one set of axes). Hint: You will first need to bin wind speeds in 1 m/s increments and calculate the probability of each binned value of wind speed using the Weibull CDF for both sets of Weibull parameters. Use this information to determine the number of hours per year that the wind is blowing within each bin range. [4 marks] (ii) Calculate the annual energy production and capacity factor for a single turbine installed at the wind site. [3 marks] Table 3. Wind Power Table Data [http://wind-data.ch/tools/powercalc.php]
ST337 and ST405 BAYESIAN FORECASTING AND INTERVENTION Summer 2024 1. In a clinical trial, a new vaccine is being tested. A group of n individuals are vaccinated. Record Yi = 1, if the individual i has successfully developed immunity. Assume that Y1, . . . ,Yn are conditionally independent given the unknown success rate θ. (a) Which of the probability distributions listed in Table 1 is best suited to model the data generation process? Write down the corresponding likelihood function. [2 marks] Table 1: Probability distributions of random variable Y . B(α, β) and Γ(α) are the Beta and Gamma functions, respectively. (b) Suppose a scientist wants to use the uniform. distribution as the prior for θ. (i) As the scientist has no preference toward any specific value of success rate θ, specify suitable values of parameters a and b of the uniform. prior? Justify your answer. [2 marks] (ii) Would the Uniform. prior be conjugate for the selected likelihood function? Justify your answer. [3 marks] (c) Another scientist wants to use a Beta(α, β) as the prior for θ. (i) Derive the posterior distribution for θ given Y1:n and state explictly the distribution parameters. [4 marks] (ii) Find the posterior mean and variance (in terms of n, α, β, and y¯ = n−1 yi) [4 marks] (iii) Give the expression (pdf) of the prediction distribution of Yn+1 given Y1:n (Hint: the expression is a fraction of two Beta functions B(·, ·)). [5 marks] 2. Consider a Constant Gaussian Dynamic Linear Model (DLM) used to track the position and velocity of a high-speed train. Denote k ≥ 0 as the evenly spaced discrete time index, with time step ∆. The state of the system at time k is given by the state vector θk = (θk,1, θk,2) ⊤, which includes the position θk,1 and velocity θk,2 of the train. The transitions of train’s states are subject to transition noise uk. Sensor measurements of the train’s position Yk are recorded at discrete time and are subject to observation noise vk. (a) Write down the corresponding state (including the initial state) and observation equations and give explicitly the assumptions on the noise terms. [3 marks] (b) What are the hyperparameters of the DLM and their dimensionality? Give the explicit form. of the transition matrix and the observation matrix. [2 marks] (c) Assuming the conditions of Kalman Filter are fullfilled. Let the predictive distribution of θ1 given Y0 and the noise covariances be expressed respectively as θ1|Y0 ∼ N((0, 0)⊤, I2), cov(uk) = I2, and cov(vk) = 1, where I2 is 2 × 2 identity matrix. (i) Write down the forecasting distribution of Y1 given Y0. [3 marks] (ii) Given the observation y1 = 2, write down the filtering distribution of θ1 given Y0:1. [3 marks] (iii) Compute the Kalman gain K1. Compare the predictive distribution of θ1 given Y0 and the filtering distribution of θ1 given Y0:1. Explain the implications of using the filtering distribution over the predictive distribution for making real-time decisions. [3 marks] (d) Suppose the observation at time k+1 is missing, derive the predictive distribution of the state θk+2, given all observations up to time k (in terms of mk+1 and Pk+1). [2 marks] (e) Suppose U and V are unknown covariances of transition and observation noises, respectively. U and V have common scaling factor σ 2 , and they can be be written as U = σ 2Ue and V = σ 2Ve with σ 2 unknown but Ue and Ve known. We perform. inference on σ 2 using both Maximum likelihood estimation (MLE) and Bayesian approach. (i) For MLE, write down the likelihood function and explain why the Kalman filter is useful to obtain MLE. [2 marks] (ii) For Baysian approach, without actual derivations, state the prior distribution and indicate what family the posterior distribution of σ 2 will belong to. [2 marks] 3. (a) Consider M a univariate time-series {Yk, θk}k≥0 of the form. with θ0 = u0 and, for k ≥ 0, vk and uk have mean zero and are all independent. (i) Derive the expression of the forecast function gk(δ) = E(Yk+δ | Y0:k) of M, for δ > 0, in terms of the filtering mean mˆ k = E(θk | Y0:k). Justify all steps. [3 marks] (ii) Consider the transition matrix the observation vector H = (1, 1) and the filtering mean mˆ k = (2, 2)⊤. 1) Find the forecast function. 2) Is this DLM a polynomial model? 3) Is this DLM observable? [4 marks] (iii) Can a DLM similar to the one in Question 3(a)(ii) have forecast function gk(δ) = 3δ? Justify your answer. [2 marks] (iv) Why the DLMs are classified based on E(yk+δ | y0:k) and not on E(θk+δ | y0:k)? [2 marks] (b) Consider an M with transition matrix and with observation matrix H = (2, 1). (i) Provide the expression of M′ a canonical similar model to M and identify its transition matrix F ′ and its observation matrix H′ . Justify your answers. [3 marks] (ii) Find the similarity matrix S between M′ and M. [4 marks] (iii) Let Id2 denote the 2 × 2 identity matrix. Since F = Id2F ′ Id− 2 1 , can you conclude that the similarity matrix is S = Id2, without performing the calculations in Question 3 (b)(ii)? Justify your answer. [2 marks] 4. Consider a DLM of the following form. with θ0 = u0, F = , H = [1 0] and uk = ϵk, with {ϵk}k≥0 be a sequence of i.i.d. random variables distributed as N(0, σ2 ) for some σ 2 > 0. (a) Rewrite this DLM as an ARMA(p, q) model for Yk. Identify the value of p and q, as well as the AR and MA coefficients. [5 marks] (b) Study the stability of the time series using the characteristic polynomials. Without further calculations, compare your result with the stability conditions for an AR(1) process and conclude about the influence of the moving average component. [5 marks] (c) Write the ARMA(p, q) model obtained in Question 4(a) as an infinite-order MA process. [4 marks] (d) Let Zk = Yk − E(Yk|Y0:k−1) be the innovation. (i) What is the mean of Zk. Justify your answer. [2 marks] (ii) What is the variance of Zk given Y0:k−1. Justify your answer. [2 marks] (iii) What is the lag-δ autocvariance of Zk for δ ≠ 0. Justify your answer. [2 marks] 5. (a) Consider a real-valued distribution π with mean µ = E(θ) and variance where θ ∼ π. Suppose µ is known, but σ 2 is unknown. The task is estimating the variance σ 2 . (i) Derive the expression for Vb1, the Monte Carlo estimator of σ 2 , and express its variance in terms of the fourth central moment µ4 = E[(θ − µ) 4 ] and the variance σ 2 of π. [2 marks] (ii) Derive the expression for Vb2, an importance sampling estimator of σ 2 with respect to a general proposal distribution q. [2 marks] (iii) Compute the mean and variance of Vb2. [3 marks] (iv) Is it possible for the variance of Vb2 to be lower than that of Vb1? Justify your response and provide an illustrative example [Hint: consider sampling weights ω(θ) = σ 2/(θ − µ) 2 ]. [3 marks] (v) List two advantages of using Vb2 over Vb1, especially in light of your answer to the Question 5(d). [2 marks] (b) Consider the following smoothing distribution: with and with Zn, the corresponding normalising constant. It is assumed that one can sample from p0(·) and qk(· | θ) for any θ ∈ Θ. (i) What are the two main issues when estimating integrals related to smoothing distributions? [2 marks] (ii) Which proposal distribution sn(θ0:n) would you use? [2 marks] (iii) Propose a way to get a sample θ0:n from sn(·). [2 marks] (iv) In the context of self-normalised importance sampling, what would be weight of this sample? [2 marks]
15–122: Principles of Imperative Computation — Summer 2019 Final Exam Friday 9th August, 2019 1 Graph Representation [C] (55 points) The graph interface seen in class and reported on page 30 could return a collection of neigh– bors of a vertex as a NULL–terminated linked list of vertices. An adjacency matrix imple– mentation had to construct this list when graph_get_neighbors was called, and later free it when graph_free_neighbors was called. An adjacency list implementation could be more efficient: it simply returned the pointer to the adjacency list of the requested vertex and did nothing to dispose of it. Task 1.1 The following client function uses the graph interface: unsigned int count_neighbors_of_0(graph_t G) { REQUIRES(G != NULL && graph_size(G) > 0); vert_list *nbors = graph_get_neighbors(G, 0); unsigned int c = 0; while (nbors != NULL) { c++; nbors = nbors->next; } graph_free_neighbors(nbors); return c; } This function, although it technically respects the graph interface seen in class, is prob– lematic. Assume we are using an adjacency matrix implementation of graphs. What is wrong with this function? Task 1.2 Here is another snippet of code – imagine that, for an unknown reason, the client wants to ignore all neighbors that are multiples of 10, so they set them to a dummy value that their later code can ignore: vert_list *nbors = graph_get_neighbors(G, 5); for (vert_list *p = nbors; p != NULL; p = p->next) { if (p->vert % 10 == 0) p->vert = 99999999; // set vertex to dummy value } unknown_function_that_hates_multiples_of_ 10(nbors); graph_free_neighbors(nbors); Again, this client code respects the graph interface but is still problematic. Now assuming we are using an adjacency list implementation, what is undesirable about this function? An approach to avoid this problem altogether is to make the type of values returned by graph_get_neighbors abstract. Here are the relevant parts of a modified graph interface that does precisely that: it exports the type neighbors_t without revealing how it is defined. The new function neighbors_next returns the next vertex in a collection of neighbors, assuming that there are more neighbors. The function neighbors_empty checks this property. Therefore, we can now get a collection of neighbors by calling graph_get_neighbors, and then repeatedly check whether it is empty and ask for the next neighbor if not. C0–style contracts are included for readability. //typedef ________ *neighbors_t ; // NEW neighbors_t graph_get_neighbors(graph_t G, vertex v); // UPDATED //@requires G != NULL; //@requires v < graph_size(G); //@ensures result != NULL; vertex neighbors_next(neighbors_t N); // NEW //@requires N != NULL && ! neighbors_empty(N); bool neighbors_empty(neighbors_t N); // NEW //@requires N != NULL; void neighbors_free( neighbors_t N); // UPDATED //@requires N != NULL; Task 1.3 Using this new graph interface, modify the code given on page 1 for the client function count_neighbors_of_0 to prevent the problem you unveiled in task 1. int count_neighbors_of_0(graph_t G) { REQUIRES(G != NULL && graph_size(G) > 0); int c = 0; neighbors_t nbo rs = ; while ( ) { c++; ; } ; return c; } We will now update the adjacency list implementation of the graph interface to match the new interface. Recall the definition of the internal type graph: typedef struct adjlist_node adjlist; struct adjlist_node { vertex vert; adjlist *next; }; typedef struct graph_header graph; struct graph_header { unsigned int size; adjlist **adj; }; You may assume that the specification functions is_graph and is_vertex have been defined for you. We concretely define the type neighbors_t as follows: struct neighbor_header_adjlist { adjlist* next_nbor; }; typedef struct neighbor_header_adjlist nbors_AL; typedef nbors_AL *neighbors_t; // for the client The type nbors_AL contains one field, which is a pointer to the node which contains the next vertex that should be returned (or NULL if there are no more neighbors). Task 1.4 Implement the function graph_get_neighbors so that it has constant cost. Include ap– propriate contracts. nbors_AL *graph_get_neighbors(graph_t G, vertex v) { } Task 1.5 Implement the function neighbors_empty which checks whether there are any more neighbors in a collection. Include appropriate contracts. bool neighbors_empty(nbors_AL *N) { } Task 1.6 Implement the function neighbors_next, which also should have constant cost. It should not allocate any additional memory. Include appropriate contracts. vertex neighbors_next(nbors_AL *N) { } Task 1.7 Complete the body of neighbors_free so that, by the time we are done using a graph, all allocated memory has been freed and none has been freed twice. void neighbors_free(nbors_AL *N) { } Next, we will do the same with the adjacency matrix implementation of the graph interface. The internal types graph and nbors_AM are now defined as follows: typedef struct graph_header graph; struct graph_header { unsigned int size; bool **adj; // 2-D array }; struct neighbor_header_adjmatrix { bool * row; unsigned int length; unsigned int next; }; typedef struct neighbor_header_adjmatrix nbors_AM; typedef nbors_AM *neighbors_t; // for the client Neighbor collections (right) are defined as a struct containing a pointer to the row of the matrix for the vertex whose neighbors we are considering, the length of this array, and the index of the next cell of this array that contains a neighbor of the vertex. This means you’ll need to very carefully consider how to set the next field in both graph_get_neighbors and neighbors_next. 7pts Task 1.8 Implement the function graph_get_neighbors. Include appropriate contracts. nbors_AM *graph_get_neighbors(graph *G, vertex v) { } What is the worst–case complexity of graph_get_neighbors as a function of the number v of vertices and the number e of edges in the input graph? O( )
ETB1100 Assignment: A Regression Analysis The Value of Linear Relationships for Decision Making in Business Learning Objectives (LO): LO1: Understand how to use Excel to draw a random sample of data LO2: Develop a simple linear regression model using EXCEL LO3: Understand simple linear regression analysis including assessing the validity of the model and interpreting the findings. LO4: Develop the ability to analyze and interpret multiple linear regression models using ChatGPT as a collaborative tool for extending statistical knowledge. LO5: Describe the business implications of your multiple linear regression analysis. Submission Details: • This assignment is marked out of 72 and worth 10% of the assessment for this unit. • It is designed to test learning Objective 4: “Interpret and evaluate relationships between variables for business decision-making, using the concept of correlation and simple linear regression. ” Due Date: 11:55pm, Sunday 13th October, 2024. • You must submit your completed assignment (including the Assignment Coversheet, correctly filled in AND signed), on-line via the Moodle site for this unit. • Name the soft copy of your assignment as follows: Student ID_Surname_Initial.doc (this should include all tables, charts, exhibits etc produced using EXCEL) • DO NOT submit any EXCEL files (You should have already copied any relevant EXCEL output and pasted it into your Word document). • SUBMIT ONLY ONE FILE. • Upload this file on Moodle any time PRIOR to the deadline. (After this time, the upload link will be closed). • You will find the upload link in the ASSESSMENTS section on Moodle. • Click on the “ Click Here to Upload Assignment” link to upload. • Once you have uploaded and saved, the following message will appear momentarily, “File uploaded successfully.” To confirm your upload was successful, you will then see your uploaded file’s name. • A penalty of up to 5% of the marks earned may apply for each day an assignment is late unless an extension of time has been sought. Extensions will only be granted for substantive reasons at the discretion of the Chief Examiner and must be applied for before the assignment is due. • Please retain your own copy of the assignment until after the publication of final results for this unit. Beyond the Haze: Decoding the True Impacts of Vaping Using Regression Analysis to Investigate the Consequences of Vaping. https://theconversation.com/vaping-now-more-common-than-smoking-among- young-people-and-the-risks-go-beyond-lung-and-brain-damage-223125 The Assignment Brief: With the increasing popularity of vaping comes the need for a critical review of the real health consequences hidden behind the enticing clouds of vapour. To this end, you have been provided with some relevant data and are tasked with analysing it using correlation and regression analysis. There are SIX parts to this analysis: 1. Background (AI and Generative AI tools are required to be used in this Part). 2. Sample Acquisition [LO1] (AI and Generative AI tools must NOT be used in this Part because it requires students to demonstrate human knowledge and skill in using EXCEL). 3. Model Development - Correlation Analysis & Simple Linear Regression [LO2] (AI and Generative AI tools must NOT be used in this Part because it requires students to demonstrate human knowledge and skill in using EXCEL). 4. Model Validation and Interpretation-Simple Linear Regression [LO3] (AI and Generative AI tools must NOT be used in this Part because it requires students to demonstrate human knowledge and skill in using EXCEL). 5. Analysis Extension to Multiple Linear Regression [LO4] (AI and Generative AI tools may be used selectively within this Part as per explanation provided). 6. Conclusions and Business Implications [LO5] (AI and Generative AI tools may be used selectively within this Part as per explanation provided). The data comprises 100 observations across six variables that provide a comprehensive view of individual vaping habits and their potential impacts on lung health in Australia. The variables are labelled Lung Function Score (Y): • Definition: A numerical score ranging from about 40 – 150 that represents the lung health of the individual, with higher scores indicating poorer lung function. • Unit: Score (no specific unit, higher scores indicate worse health) Scores closer to 40 suggest better lung health, scores approaching or exceeding 100 suggest significantly impaired lung function) Nicotine Concentration (mg/mL) (X1): • Definition: The concentration of nicotine in the e-liquid used in the vaping device. • Unit: Milligrams per millilitre (mg/mL) Years of Vaping (X2): • Definition: The total number of years the individual has been using vaping products. • Unit: Years Daily Usage Frequency (X3): • Definition: The average number of times per day the individual uses vaping products. • Unit: Times per day Number of Flavors Used Regularly (X4): • Definition: The number of different flavours of e-liquid that the individual uses on a regular basis. • Unit: Count (no specific unit) Age of Vaping Initiation (X5): • Definition: The age at which the individual first started using vaping products. • Unit: Years and can be found in the data file labelled Vaping Health Impact.xlsx under the ASSIGNMENTS heading in the ASSESSMENTS section on Moodle, and is to be used to answer the questions listed here. Assume that the population from which this data was drawn, was approximately normally distributed. NOTE: All relevant EXCEL output must be copied and pasted into a single document (.docx) for submission. DATA PREPARATION AND 6EXCEL HYGIENE,. In all lecture examples involving the use of EXCEL, as well as the solutions to tutorial questions, I have been very particular about how to format the data, clean up the output (e.g. adjust to four decimal places, label everything, edit the charts etc etc). This is because this ‘EXCEL hygiene’ is essential in the workplace and also highly valued. Generating output is easy, consistently ensuring it is clearly labelled and easy to identify, understand and track, is more difficult, simply because it takes more time. This time is worth the investment and will be expected in your report. PART ONE: Background (4 marks) (AI and Generative AI tools are required to be used in this Part). Write an introductory paragraph about vaping in Australia with the objective of providing some context for this assignment. You are required to use generative AI software, such as Chat GPT. It must be strictly no more than 200 words, and you must provide the prompt and prompt refinements you used, and of course, footnote your source. Screenshots of ChatGPT output are acceptable. Guideline for footnoting source: “ChatGPT's Explanation of … … … … … … .," Generated by ChatGPT-3.5, OpenAI, September 15, 2021, [URL of the Chat or Platform] Q1 NOTE 1: Prompts will be assessed using the 3C’s criteria (0.5 mark each): 1. Clarity: Clear prompts prevent confusion and guide AI effectively 2. Context: Rich context avoids incomplete or inaccurate outputs 3. Creativity: Open-ended prompts encourage diverse and innovative content Q1 NOTE 2: Response will be assessed using the following criteria (0.5 mark each): 1. Relevance: Does the response directly address the prompt's intent and context? 2. Coherence: Is the response logically organized and structured, ensuring easy comprehension? 3. Completeness: Does the response cover all relevant aspects of the prompt or leave critical gaps? 4. Accuracy: Are the facts, information, and details presented in the response correct and reliable? 5. Appropriateness: Is the tone, style, and language of the response suitable for the intended audience? PART TWO [LO1]: Sample Acquisition: (5 marks) (AI and Generative AI tools must NOT be used in this Part because it requires students to demonstrate human knowledge and skill in using EXCEL). Begin your analysis by using the Random Sampling procedure demonstrated in both the lecture and tutorials in Week 10, to select a RANDOM SAMPLE of 80 observations from your data and copy and paste all EIGHT variables (Observation Number, Lung Function, Nicotine, Years of Vaping, Daily Usage, Flavours, Initiation Age, Random Number)) into a separate worksheet labelled, ‘Sample_80’, in columns B - I respectively. In column A, you are to number the rows (1- 80) and label this column ‘Count’ . Include a screenshot of this ‘Sample_80’ worksheet here to demonstrate you have sampled correctly. Label this as EXHIBIT 1 and include a relevant title. PART THREE [LO2]: Model Development-Correlation Analysis & Simple Linear Regression (22 marks) (AI and Generative AI tools must NOT be used in this Part because it requires students to demonstrate human knowledge and skill in using EXCEL). (a) Use EXCEL to produce a correlation matrix for all variables (dependent and independent), remembering to follow the approach demonstrated in Lecture 10. (3 marks) (b) Now use this correlation matrix to identify which independent variable has the strongest relationship with Lung Function (Y) to be used later in a regression model. State which variable this is and what evidence led you to choose it. (3 marks) (c) To investigate if a linear relationship is a reasonable assumption, use EXCEL s scatterplot option to produce a graph of these two variables. Include the line of best fit (DO NOT INCLUDE R2 it is not to be discussed here). Label this graph as EXHIBIT 3 with a relevant title and remember to optimise its presentation via the various formatting options available. (4 marks) (d) Based ONLY on the scatterplot you produced as EXHIBIT 3, does a linear relationship seem reasonable? If so, is it a positive or negative slope? Provide evidence for your answer and interpret what this means in context of this question. (4 marks) Regardless of your answer in (d), now assume that a linear relationship is reasonable. (e) Using the Regression Analysis procedure in EXCEL, produce a simple linear regression model of Y vs X1, with the following requirements: • Select 99% Confidence Level in the Output Options. • Report all values to 4 decimal places where relevant. • Provide the Summary Output labelled as EXHIBIT 4 with an appropriate title. (4 marks) (f) Based on this output, state the equation of this regression model (correct to 4 decimal places), remembering to define the variables. (4 marks) PART FOUR [LO3]: Model Validation and Interpretation-Simple Linear Regression (18 marks) (AI and Generative AI tools must NOT be used in this Part because it requires students to demonstrate human knowledge and skill in using EXCEL). Before interpreting this model, it is first essential to determine whether or not it is a true representation of the relationship that exists in the population between Lung Function (Y) and the independent variable (X1) you selected in (b). To do this, a hypothesis test of significance is required. (a) Using a 5% level of significance, determine whether or not this relationship between Lung Function (Y) and the independent variable (X1) you selected in (b), is a statistically significant, linear relationship. Ensure that you clearly state your hypothesis, show ALL steps, ALL working AND interpret your conclusion IN CONTEXT of this question. (6 marks) Assuming now that the model you have identified is statistically significant, it is time to interpret the model. (b) State and provide an interpretation of the Y intercept, b0 and the slope coefficient, b1 . (7 marks) (c) State and interpret the coefficient of determination for this model, in context of this question. (5 marks) PART FIVE [LO4]: Analysis Extension to Multiple Linear Regression (15 marks) (AI and Generative AI tools may be used selectively within this Part as per free text explanation provided). (a) Using the Regression Analysis procedure in EXCEL, now include ALL FIVE independent variables in the regression against Lung Function (Y) to produce a Multiple Linear Regression model. Label the regression output as EXHIBIT 5 with a relevant title, and remember to optimise its presentation via the various formatting options available (TIP: it is not ‘user-friendly’ for management if you leave any scientific notation in the output). (3 marks) (b) From what you have learned about Simple Linear Regression analysis, discuss what the Multiple Linear Regression output you produced in EXHIBIT 5 tells you? SPECIAL INSTRUCTIONS: Through our examination of Simple Linear Regression, we covered most of what you need to know to understand a Multiple Linear Regression model – however, not everything. Use ChatGPT to help you fill in the gaps for this Multiple Linear Regression part of the assignment. This does not mean you should use ChatGPT to do everything – if that was my intention, I would have said that. Instead, I want to see how you can work WITH ChatGPT as your assistant, not boss. For this part, you will be assessed on how you interact with ChatGPT, what prompts you use and then how you refine those prompts, review and add to the responses, draw your own conclusions from the responses etc. You will be assessed less on the accuracy of your discussion and more on your intellectual engagement with the ChatGPT process and output. If you choose poorly and use ChatGPT to do everything, with little to no involvement from yourself, you will be penalised heavily and likely score zero for this Multiple Linear Regression part of the assignment. My whole intention is to provide the opportunity for you to harness the power of ChatGPT whilst remaining in the driver’s seat. This will be a critically important experience for you, should you choose to accept it with integrity. GUIDANCE TO FOLLOW: 1. Even before calling on help from ChatGPT, you should comment on the things you already know about: R-Square and the p-values for each of the coefficients. 2. Next, you should be curious about how to interpret each of the coefficients, now that there is more than one – ask for help from ChatGPT 3. And what about the ‘Adjusted R-Square’ – is that relevant and why? Ask for help from ChatGPT – remember, the better your question, the better the answer! 4. And, is the ‘Significance F’ value relevant? Ask for help from ChatGPT Be sure to include in your answer whether or not the multiple linear regression model is better than the simple linear regression model and be able to explain why? How? (12 marks) PART SIX [LO5]: Conclusions and Business Implications (5 marks) (AI and Generative AI tools may be used selectively within this Part as per explanation provided). Time To Deliver Your Expert Opinion on This Matter Now, referring ONLY to the Multiple Linear Regression analysis, that is, what you found and discussed in PART FIVE, in 200 words or less, describe what the business implications of your findings might be? There will be many possible correct answers to this question, but the ones you present, must be consistent with your findings and context. If you choose to get some help from ChatGPT, as always, you must provide the prompt and any prompt refinements you used, and of course, footnote your source. PRESENTATION: (3 marks) There are 3 marks available for presentation. These marks will be awarded for things such as: Easy to read; logical flow of answers; cohesive report; answers clearly labelled; appropriate font size, borders, colour choice, labelling of graphs, care in spelling, grammar and punctuation. ASSIGNMENT TOTAL = 4 + 5 + 22 + 18 + 15 + 5 + 3 = 72 MARKS
School of Psychology & Humanities 2023-24 EB3891 Research Methods for International Business Communication ASSESSMENT BRIEF Written Assignment: Small Research Project 3,000 words (worth 70% of total module mark) Presentation: Size 12 font + double-spacing Report format: use the headings below Firstly, write your research title (problem to solve/ question to answer) in a header at the top of your work. Introduction (250 words) Chapter 1: Literature Review (approx. 1,000 words) Chapter 2: Research Methods (500 words) Chapter 3: Discussion of Findings (refer to data in appendices) (approx. 1,000 words) Conclusion (250 words) Reference List-not included in word count Appendices to include Data Presentation- not included in word count CHAPTER 1: LITERATURE REVIEW- approx. 1,000 words As we discussed in class, your literature review will form. the foundation on which your research project is built. Its main purpose is to give you a good understanding of relevant previous research. Your literature review should help you further refine your research questions/objectives and to highlight research possibilities that have been overlooked / underdeveloped in the existing literature. When writing your literature review you will need to: · Include the key academic theories in your chosen area. · Demonstrate that your knowledge in this area is up-to-date. · Show how your research relates to what is already there. · Assess the strengths and weaknesses of work already published. Adapted from: Saunders et al. (2004) Research Methods for Business Students, Essex; Prentice Hall. Also, look at: Machi & McEvoy (2016) The Literature Review: six steps to success, London, Corwin. The Structure of your Literature Review Your Literature Review will have a brief introduction outlining the structure of this first chapter of your dissertation. There will then follow a series of numbered sections and sub-sections on relevant topics. These may be ordered in a number of ways, for example: · From general to more specific areas. (Imagine the shape of a funnel to help you with this) · Chronologically from earlier to later theories / concepts · According to different ‘schools of thought’ (ideas / perspectives) on the topic. · In relation to arguments and counter-arguments. In the Research Proposal, we asked you to prepare a plan of your Literature Review, including the headings and sub-headings that it might include. You may like to follow through now with this planned structure in your Literature Review. On the other hand, it may be that you have further refined your plan based on the greater reading that you will by now have done. Both of these options are acceptable. To help you with your Literature Review, you may like to review the slides from the previous sessions of EB3991 Research Methods module on Blackboard. You may also like to refer to some of the Research Methods publications on the EB3991 reading list online. Do not begin sentences with And But or Because. Do not use etc or and so n=poor style. No contractions, i.e. isn’t →is not Use paragraphing Do not use about-use with regard to Do not confuse to analyse=verb with analysis=noun USE PHRASES LIKE: The literature around this subject area focuses on….. The literature shows that …………………. The literature indicates that……………….. After reviewing the literature, it can be categorised into two/three main schools of thought. These can be summarised as follows………… There are writers who have one perspective….., and others who believe…….. There appears to be a gap in the literature around……………….. You should demonstrate a good understanding of the published literature supporting your research topic. You should demonstrate the level of your understanding by your ability to make critical comments on the strengths and weaknesses of the main theories/issues covered in your literature review. You should demonstrate how your work fits in to what has already been published. There is no one correct way to structure a Literature Review, although some examples of ways you might do this are presented above. You should therefore demonstrate that you are able to structure your Literature Review in a logical way to support the aim/objectives/hypothesis of your study. You should reference your Literature Review fully, using the Harvard Referencing system and present a references’ page and a bibliography. (Remember you use a references page for direct references, and a bibliography for publications you have read but not quoted or paraphrased) You should demonstrate that you are able to clearly communicate your Literature Review in the English language. Please find the link below to the academic language resource http://www.phrasebank.manchester.ac.uk/ CHAPTER 2: Research Methodology · PROVIDE YOUR RESEARCH TITLE · LIST AND NUMBER YOUR RESEARCH QUESTIONS (ABOUT 2-3) · Research Methodology Chapter to include the following: Research questions · Discuss a justification of your research approach and why it is appropriate for the topic research questions (quantitative –objective-deductive-explains data or qualitative-subjective-inductive understands/interprets data or mixed and why most appropriate for this topic/study- referring to various authors on research methods opinions-do not discuss lit review in this section-Also discuss different criteria for evaluation and judgement of approach and strategy chosen either validity/reliability and generalisability-Creswell (2009), Quinlan (2011) Bryman and Bell (2007) OR Trustworthiness-credibility, dependability, transferability and confirmability -Lincoln and Guba (1985) Silverman (2013)-authenticity, honesty · Design of research tools (e.g. focus group/survey/questionnaire/interview (Kvale 2008) and type/observation-referring to various authors) · Piloting research tools (how this is done to resolve any mistakes before going live) · Sampling (why did you choose the study population-size, gender, age, nationality) · Any ethical issues (researcher identity, bias, respondent confidentiality and anonymity…) · Problems and/or limitations of this study (time, access, opportunity, lack of resources) · Data collection process (a description) · Findings · A mini chapter conclusion (Did your choice of research approach and strategy together with methods/tools help to answer your research question. Is there a ‘fit’? or could it have been done better. How rigorous is this study and does it demonstrate good scholarship? (Grix 2002)) CHAPTER 3: Discussion of Research Findings and Conclusion (refer to Appendices- Presentation of data results) · Appendices-You should report your data results in a logical and coherent way, illustrating with tables, graphs, and diagrams, quotations from interview transcripts or whatever is appropriate to your particular study. You should aim to clearly communicate your key findings to your reader. You should present your data results in an appropriate format. · Discussion of findings-You should discuss your results in the context of your literature review, showing where your findings agree, disagree, or add to what has already been written about your topic. You should demonstrate high quality analysis of your data and of how your results fit with what has already been written about the topic. · Communicate your conclusions clearly and summarise all the key points discovered through your secondary/primary research · You should demonstrate an ability to communicate effectively in the English language. · You should carefully reference your work using the Harvard referencing system. Submission date: Monday 22nd March 2024 at 1pm: in electronic form. to Turnitin via Blackboard Final checks for your assignment: · Ensure you have included all the key components in the correct order · Proof-read your work for content, structure and flow first, · Then check your English grammar again before submission · Ensure that the in-text referencing is faultless and that all references correspond fully to the reference list at the end. Do not confuse authors’ surnames with their first names. · The reference list must be full and organised alphabetically, according to the Harvard Referencing standard on the Cite Them Right website.
Minimalism Test 2 Beat-class sets, Rhythmic Patterns, Layering, Resulting Patters KM Spring 2024 Question 1 A) On the staff, notate the following rhythmic pattern in 12/8, Modulus 12, expressed in integers: (0 1 1 0 1 1 0 1 0 1 1 0) B) Notate the following rhythmic pattern in 12/8, Modulus 12, using integers: For quarter notes, notate the attacks (using integer 1) followed by an 8th note rest. C) Notate the intersections of the following two patterns using integers: for quarter notes, notate the attacks (using integer 1), followed by an 8th note rest. D) Notate the union of the same two rhythmic patterns using integers: for quarter notes, notate the attacks, followed by an 8th note rest. Question 2 A) On the staff, transpose Pattern A by one 8th note (T1), and copy the original version of Pattern B. Circle intersections and write the resulting pattern using integers. Please note that with shifting the pattern by one, your last 8th note will move to the beginning of the measure (pulse 1). B) Compare the resulting patterns of the two intersections (original version and the version with Pattern A, T1) and briefly comment similarities and differences. Does shifting of Pattern A change the perception of meter? C) Notate the intersections of all three lines, Pattern A, Pattern B, Pattern C. Write the resulting Beat-class set using integers. Compare it with the original Intersection of patterns A and B. Briefly describe the result. Did the perception of meter of downbeat change? Question 3 A) Using integer notation, write the beat-class set of the upper layer (the third E-G in the top voice, Pattern A in the example), and the beat-class set of the lower layer (the chord). B) Identify the relationship between Pattern A and Pattern B. Are they the same? Briefly explain. C) The previous example is in Modulus 10. Compose two rhythmic patterns in 10/8. Their intersections should produce the following Bc-set. (1 0 1 0 0 1 1 0 0 0) Please note that multiple answers possible.
ST3370_B BAYESIAN FORECASTING AND INTERVENTION Summer 2023 Question 1 We are given three observations about the temperature in January in London, namely y1 = 1; y2 = −2; y3 = 5 degrees Celsius. We know that the historic average January temperature is 0 degrees and we want to make inferences on its variance by modelling the observations as realizations of y1:n = (y1, . . . , yn) which, given a random parameter θ, are conditionally independent and identically distributed random variables. We recall the expressions of the following three densities: • Normal distribution of parameters (µ ∈ R, σ2 > 0) • Generalized Student’s T distribution of parameters (ν > 1, ˆµ ∈ R, ˆσ > 0) • Gamma distribution of parameters (α > 0, β > 0) (a) Consider the following likelihoods for the observations: 1. fyi|θ(y | θ) = N(y; θ, 1); 2. fyi|θ(y | θ) = N(y; 1, θ); 3. fyi|θ(y | θ) = N(y; 0, θ); 4. fyi|θ(y | θ) = St(y; ν, 0, θ) with ν > 1; 5. fyi|θ(y | θ) = Ga(y; θ, β0) with β0 > 0. For each of them, argue whether it represents a good choice for the likelihood, considering both the information at your disposal and the tractability of the model. [Hint: the mean of St(y; ν, ˆµ, ˆσ) is µˆ if ν > 1] [5 marks] (b) Consider the following likelihood and prior for the precision parameter: Recall that if x ∼ Ga(x; α, β), the expected value of x is E(x) = α/β and its variance is Var(x) = α/β2 . (i) What is the prior opinion on the average precision of the data? [1 mark] (ii) Derive the posterior pθ|Y1:3 justifying every step. [3 marks] (iii) Interpret the information contained in the posterior about the average precision of the temperature by comparing it with the prior guess. [2 marks] (c) Consider the same likelihood fyi|θ(y | θ) and prior for θ as in Question 1(b). (i) Derive the distribution of y1 justifying every step. [3 marks] (ii) Using the properties of the conditional moments, find the mean and variance of y1 using the fact that E(1/θ) = ∞. [2 marks] (iii) Use the findings of Question 1(c)(i) and Question 1(c)(ii) to argue to which extent the distribution of a conditionally normal observation is similar to a normal distribution. [2 marks] (iv) Another set of readings of the temperature in the same experimental conditions provides you with observations z1 = 0.9; z2 = −2.1; z3 = 5.2 degrees Celsius. You decide to implement a new model whose observables are the mean of the readings xi = (yi + zi)/2. Argue whether you should use the same parameters for the likelihood and the prior as the ones in Question 1(b). [2 marks] Question 2 Consider a general state-space model (θk, yk)k≥0, where (θk)k≥0 are the parameters and (yk)k≥0 are the observables. (a) (i) State the assumptions on (θk)k≥0 and (yk)k≥0. [2 marks] (ii) Express the filtering distribution pθn|y0:n in terms of the likelihood pyn|θn and the predictive pθn|y0:n−1 justifying all steps. [3 marks] (b) Assume that the state-space model (θk, yk)k≥0 is a d-dimensional dynamic linear model (DLM) of the form. with θ0 = u0 and, for k ≥ 0, vk and uk have mean zero and are all independent. (i) Provide the expression of the predictive mean E(θn | y0:n−1) in terms of the filtering mean E(θn−1 | y0:n−1) justifying all steps. [2 marks] (ii) Provide the expression of the predictive variance Var(θn | y0:n−1) in terms of the filtering variance Var(θn−1 | y0:n−1) justifying all steps. [2 marks] (iii) Can the predictive distribution pθn|y0:n−1 have the same mean and variance as the filtering distribution pθn|y0:n−1 ? Justify your answer using your solution to Question 2(b)(i) and Question 2(b)(ii). [2 marks] (c) Assume we are given the observations y0:n and we are interested in retrospectively reconstructing the system, i.e., in finding the smoothing distribution pθk|y0:n (θk | y0:n) for 0 ≤ k ≤ n. In the following, we assume the Kalman filter recursion is known and we aim at establishing a backward smoothing recursion. (i) Using Bayes theorem and the properties of a state-space model, show that (2.1) [Hint: explain why pθk|θk+1,y0:n (θk | θk+1, y0:n) = pθk|θk+1,y0:k (θk | θk+1, y0:k).] [4 marks] (ii) For each of the probabilities appearing in (2.1), explain why they are known. [2 marks] (iii) Use (2.1) to express pθk|y0:n (θk | y0:n) in terms of an integral depending on pθk+1|y0:n (θk+1 | y0:n) and other known probabilities. [3 marks] Question 3 Consider M a univariate time-series (θk, yk)k≥0 of the form. with θ0 = u0 and, for k ≥ 0, vk and uk have mean zero and are all independent. (a) (i) Derive the expression of the forecast function gk(δ) = E(yk+δ | y0:k) of M, for δ > 0, in terms of the filtering mean mˆ k = E(θk | y0:k) justifying all steps. [3 marks] (ii) Provide the definition of the observability matrix and explain in your own words why it is important for it to be invertible. [2 marks] (iii) Consider the transition matrix the observation vector H = (1, 1) and the filtering mean ˆmk = (3, 3)t . Find the forecast function and compare it to the one of a polynomial model. [2 marks] (iv) Can a DLM similar to the one in Question 3(a)(iii) have forecast function gk(δ) = 3δ? Justify your answer. [2 marks] (v) Why do you think we have classified DLMs based on E(yk+δ | y0:k) and not on E(θk+δ | y0:k)? [1 mark] (b) Consider M with transition matrix and with observation matrix H = (2, 1). (i) Provide the expression of M′ a canonical similar model to M and identify its transition matrix F ′ and its observation matrix H′ . Justify your answers. [2 marks] (ii) Find the similarity matrix S between M′ and M. [4 marks] (iii) What would change in your answer to Question 3 (b)(ii) if H = (0, 1)? [2 marks] (iv) Let Id2 denote the 2 × 2 identity matrix. Since F = Id2F ′ Id− 2 1 , can you conclude that the similarity matrix is S = Id2, without performing the calculations in Question 3 (b)(ii)? Justify your answer. [2 marks] Question 4 Consider (yk, θk)k≥0 to be a dynamic linear model of the form. with θ0 = u0 and, for k ≥ 0, uk = (ϵk, ϵ′k, 0), where (ϵk)k,(ϵ′k)k are all uncorrelated, have mean zero and constant variance σ 2 . Define ˜yk = (1 − B) 2yk where B is the backshift operator. (a) (i) Let θk = (θk,1, θk,2, θk,3). Show that y˜k = θk−2,3 + ϵk − ϵk−1 + ϵ ′ k−1 justifying all steps. [2 marks] (ii) Show that m = E(y˜k) does not depend on k justifying all steps. [2 marks] (iii) Show that v = Var(y˜k) does not depend on k justifying all steps. [2 marks] (iv) Show that for δ > 0, cδ = Cov(y˜k, y˜k−δ) does not depend on k, justifying all steps. [3 marks] (v) Use your findings in Question 4 (a) (i)–(iii) to describe the type of data that could be modelled with an ARIMA model of polynomial order 2. [3 marks] (b) Assume you do not know the distribution of (yk, θk)k≥0 but you know that (˜yk)k is a stationary process and you decide to model it with an AR(2) time series model where ϵk is a sequence of mutually-independent random variables such that ϵk ∼ N (0, σ2 ) for any time step k. (i) Find the polynomial ϕ(x) such that ϵk = ϕ(B)˜yk and express it in the form. where 1/α1, 1/α2 are the roots of ϕ, i.e., ϕ(1/α1) = ϕ(1/α2) = 0. [2 marks] (ii) Use Question 4(b)(i) to find real coefficients β1, β2 such that [3 marks] (iii) Use Question 4(b)(ii) to find the coefficients (ϕδ)δ of Wold’s representation of ˜yk, i.e., such that [3 marks] Question 5: Compulsory question for students taking ST405. (a) The goal is to approximate the mean of a real valued distribution π, that is, (i) Provide the expression for ˆI1 the Monte Carlo estimator of I and express its variance in terms of the variance σ 2 of π. [2 marks] (ii) Provide the expression for ˆI2 an importance sampling estimator of I with respect to a general proposal distribution s. [1 marks] (iii) Can the variance of ˆI2 be smaller than the one of ˆI1? Justify your answer and provide an example [Hint: consider sampling weights ω(θ) = I/θ]. [3 marks] (iv) Provide two reasons why ˆI2 could be preferable to ˆI1, also with reference to your answer to Question 5(a)(iii). [2 marks] (b) The goal is to approximate the smoothing distribution p(θ0:k | y0:k) of a state-space model (yk, θk)k≥0 defined as with θ0 = u0 and, for k ≥ 0, vk and uk have a normal distribution with mean 0 and variance 1 and are all independent. (i) Consider a sequential importance sampling (SIS) approximation of p(θ0:k | y0:k) with respect to a general proposal distribution sn(θ0:n). Provide the expression for the discrete distribution pˆ that approximates p(θ0:k | y0:k). [3 marks] (ii) What is the advantage of using a proposal that satisfies the Markov property? Justify your answer. [2 marks] (iii) Consider a SIS approximation whose proposal coincides with the prior for the states. It is said that there is a prior/data conflict whenever the prior gives high probability to parameters that correspond to low values of the likelihood. How does the prior/data conflict impact on the SIS approximation? Justify your answer. [3 marks] (iv) With reference to Question 5(b)(iii), provide intuition as to whether you expect to see a prior/data conflict if Y0 = 100. Explain the potential advantage of using p(θk|θk−1, yk) as the transition kernel of the proposal instead of p(θk|θk−1). [2 marks] (v) Explain what changes if instead of p(θk|θk−1, yk) one uses p(θk|θk−1, y0:k) as the transition kernel of the proposal. [2 marks]
Homework 7: Add NL Interactivity to your Uniswap UI Goal ● Add natural language interactivity to the Uniswap UI you built in homework 6. ● Natural language instructions ○ Using natural language, a user should be able to: ■ deposit, redeem, or swap ● Examples: ○ “swap 10 USDC for eth” ■ LLM should convert this to call of: ● swapExactTokensForTokens( ○ uint amountIn, ○ uint amountOutMin, ○ address[] calldata path, ○ address to, ○ uint deadline) ○ “deposit 5 Tether and 3 wbtc” ● Sequence of steps upon user entering NL instruction (NLI): ○ Convert NLI to a structure representation SR. ■ Text-to-sql is an example of this operation: ● convert from natural language to structured data ■ function calling is another example ● convert from natural language to API ○ Convert the SR into an Ethereum transaction TX. ○ Sign the TX with private key (eth_signTransaction) ○ Submit the signed TX to the Ethereum JSON RPC call (eth_sendRawTransaction). ● Metamask should open automatically to ask the user’s approval. ■ Ask app-specific data analysis questions (in contrast to low level chain-specific data analysis questions like we did with bitcoin) about the Uniswap pools based on the Uniswap event data. Use the events emitted by the contracts for the data analysis. ● Examples: ○ “what are the reserves of the tether-eth pool” ○ “how many swaps have there been so far today” ● Open source LLM: ○ User should have the option to use their own open source LLM. The UI should have a text box that a user can use to specify the URL for an open source model. ○ Your tests should show results on an open source model and OpenAI, side by side. ● Test cases (a.k.a. “test set”, or “task evaluation” in AI): ○ Ten test cases of varying difficulty that show how your system is able to answer natural language questions and follow natural language instructions for Uniswap data. ■ Diversity is critical to the quality of the test suite. ● Each test case should be significantly different from others. ● Collectively they should cover the space of possible user NL instructions. ■ Critical for quantifying current level of performance ○ Ten very hard test cases that are so hard that the system is not able to answer correctly. The purpose of this is to find the limit of what’s possible for this task. ■ The test cases should be significantly different from each other. ■ Critical for defining the scope of future improvements. ● Host your site publicly. ● Prepare an in-class presentation/walkthrough/demo for your UI on the due date. ○ NOTE: The submission will be at midnight, but the demo will be during class earlier that day. Deliverable 1. A publicly hosted URL to your UI that achieves the goals above. 2. Code repo 3. An “task evaluation” page in your UI that enables execution of your twenty test cases. a. For each test case: i. The natural language instruction ii. The correct answer for the test case. iii. A button that executes the test case. iv. The output from an open source model v. The output from OpenAI b. This evaluation page should allow users to add more test cases through the UI. i. Does not need to be persistent. Hints ● Learn about function calling (aka tool use). Consider whether function calling is easier to use than text-to-sql for this application. ● Open source models may not work as well as OpenAI out of the box. You may need to try advanced methods (e.g. chain of thought reasoning, etc) to get open source models to perform. well for this task. ● Focus on getting your system working end-to-end first before making it better and more accurate. Think about how to structure your work so that you can link all the pieces together with minimal effort. ○ For example: ■ Figure out how to get your system working for just one simple test case using only OpenAI: ● “swap 10 USDC for eth” ■ Deploy your system to a public URL. ■ Now you have a minimal working system and now it's easier to back and focus on making each part better (e.g. adding open source models). Guidelines ● You may use any UI framework or library. ● You may write a server for making the LLM calls, if you prefer, instead of doing it in the UI. Function Calling Example completion = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "What is the weather like in Paris today?"}], tools=tools ) OpenAI’s response: [ChatCompletionMessageToolCall (id= 'call_QahB4aCsj8i4veQROOIio2Q4 ', function=Function (arguments= '{"location":"Paris, France"} ', name= 'get_weather '), type= 'function ')] Means that OpenAI is telling us to invoke get_weather ( “Paris, France”) in order to get the answer to this question. Approach What prompt to use? Suppose: User: “swap 10eth for usdc” . Possible options in increasing levels of accuracy: 1. The prompt is exactly the instruction from the user. a. Example: i. Thus, the OpenAI API request would be just: 1. Message: “swap 10eth for usdc” . 2. Keep the prompt empty, but also now add a set of tools. a. Example: i. Message: “swap 10 eth for usdc” . ii. Tools: 1. swap (num_tokens_in, in_token_type, out_token_type) a. in_token_type i. description: the name of the token that we are swapping into the AMM. b. out_token_type i. description: the name of the token that we are swapping out of the AMM. Issue to address: ● The tool calling gives you the symbols or the names for the tokens, but the Uniswap function actually needs addresses. ● Converting the tool call function signature into the actual Uniswap call: ○ UniswapRouter.swapExactTokensForTokens ( ■ uint amountIn, ■ uint amountOutMin, ■ address [] calldata path, ■ address to, ■ uint deadline) ○ UniswapRouter has many different swap functions. We don’t need to use all of them. ■ We can just pick one. ○ This function is part of the UniswapRouter (not the UniswapPair) ■ UniswapPair has low-level interface to the AMM ● Expects several setup activities before the call actually works. ■ UniswapRouter does this setup for us. ○ The amountIn parameter is straightforward -- just copy value from tool call. ○ The amountOutMin can be hardwired to 0, which means no constraint how many token we get back. ■ Alternative, adjust tool call signature to mirror exactly the swapExactTokensForTokens signature. ○ A more interesting approach is to: ■ Aspirational: Write general purpose code to convert an ABI json for a contract, into the json schema tool call Open API expects. ● Part that needs some creativity: where to get the descriptions for the parameters of functions specified in the ABI. ○ One option: give the json schema with empty descriptions to the LLM and ask it to fill it in based on its knowledge. ■ Can increase chance of success: ● In the prompt we can give it the Solidity contract ○ Through code understanding, it may get some descriptions more accurately than if it just relied on its own internal knowledge ○ Through embedded comments in the Solidity contracts. ● Question: where to do the LLM call? ○ Ideally in the browser, because it says more decentralized. ○ But there are advantages to doing it server side,especially if you are building a product: ■ But can also just send logs back of user actions to address this. ○ https://webllm.mlc.ai/ ■ In-browser inference (as opposed to in-browser API call) ■ App remains truly decentralized ■ Hard part: ● May not work for cpu, in which you can default to API call ● even tho if they do have the gpu, may not be powerful enough or fast enough for the types of prompts we have. ● the main issue is GPU memory. To host powerful models, you need a lot of GPU memory. Otherwise you are limited to small models. ○ Microsoft has Phi series specifically for running smaller models. ● Sending the transaction (already did this step in hw6) ○ Now we have the solidity function call, we can create a transaction using web3 library, which can then also be used to: ■ Sign the TX with private key (eth_signTransaction, ■ Submit the signed TX to the Ethereum JSON RPC call (eth_sendRawTransaction). ● Data analysis questions ○ Option #1: Ask OpenAI the question from the user directly. ○ Option #2: One way: same text-to-sql setup, but for event (log) data from Uniswap contracts. ■ “emit *” writes easy to process logs on different action performed on Uniswap (e.g. swaps, deposit, etcs) ■ Read these events through the RPC json for ethereum for uniswap contracts and store them into sqlite ○ Option #3: Just give OpenAI the eth_getLogs directly and have it write code to call eth_getLogs ○ Option #4: Option #3, but help it with the decoding process. Example RPC call: { "jsonrpc": "2.0", "id": 0, "method": "eth_getLogs", "params": [ { "fromBlock": "0x429d3b", "toBlock": "0x429d3b", "address": "0xb59f67a8bff5d8cd03f6ac17265c550ed8f33907", "topics": [ "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef", "0x00000000000000000000000000b46c2526e227482e2ebb8f4c69e4674d262e75", "0x00000000000000000000000054a2d42a40f51259dedd1978f6c118a0f0eff078" ] } ] } Decoding eth_getLogs into Solidity Note: return data from eth_getLogs hasn’t been decoded into Solidity concepts. Will need to do that to decode these bytes into higher level concepts such as: emit Mint(msg.sender, amount0, amount1); How can we do this decoding? Option 1: Ask OpenAI. It may have learned how to decode from examples on the Internet. Option 2: Ask OpenAI to write code to decode it. Give it the Solidity contract and the json rpc call. Option 3: Find an existing library to do the decoding. Next issue: how to convert natural language instruction into code that calls our tools. Ask OpenAI: answer the user’s data analysis question about Uniswap. The question is at the bottom of this prompt. Write code to You may use these functions to do, assume they’ve been implemented. To-do: ● Check why the enum validation in the schema wasn’t honored by OpenAI tool calling
Assignment 2 Round up to 4 decimal places unless stated otherwise. Write a formula clearly to show how you get the answer. It takes up half of points mostly. DO NOT use excel functions for specific probability distributions like BINOM.DIST. Answers that did not follow the specified rules will get -0.1 pt. penalty if it’s correct. 1. The following probability distribution of job satisfaction scores for a sample of information systems (IS) senior executives and middle managers range from a low of 1 to a high of 3. (round up to 2 decimal places) Probability Job satisfaction score (x) IS Senior Executives IS Middle Managers 1 0.1 0.3 2 0.5 0.4 3 0.4 0.3 a. What is the expected value E(x) of the job satisfaction score for senior executives? (1 pt.) b. What is the expected value E(x) of the job satisfaction score for middle managers? (1 pt.) c. Compute the variance σ 2 of job satisfaction scores for executives and middle managers? (1 pt.) d. Compute the standard deviation σ of job satisfaction scores for executives and middle managers? (1 pt.) 2. Consider a binomial experiment with n = 20 and p = 0.70. Compute the following probabilities or values a. (1 pt.) P(x = 19) b. (1 pt.) P(x ≤ 18) c. (2 pt.) P(x ≥ 2) d. (1 pt.) E(x) e. (1 pt.) var(x) and σ 3. Phone calls arrive at the rate of 48 per hour at the reservation desk for Regional Airways. Assume that callers wait for their turn until connected. a. Compute the probability of receiving three calls in a 5-minute interval of time. (2 pt.) b. Compute the probability of receiving exactly 10 calls in 15 minutes. (2 pt.) c. Suppose no calls are currently on hold. If the agent takes 5 minutes to complete the current call, how many callers do you expect to be waiting by that time? What is the probability that none will be waiting? (3 pt.) 4. You participate in a charity 4-cards stud poker tournament. Use the hypergeometric probability distribution formula to determine the probability that a hand (of four cards) from a single deck of 52 cards contains one heart, one club, one diamond, and one spade (there are equal numbers of cards for hearts, clubs, diamonds, and spades in a deck). (3 pt.) Represent the probability as a fraction number with the simplest form (no common factors between numerator and denominator)
