ANTH0003/0004/0208: Introduction to Social Anthropology Essay Writing Guidelines 1. The marking criteria for this module are the Standard UG criteria which can be found in the Undergraduate Handbook on Anth Student Hub. 2. Use your initiative to select relevant readings from the reading list. You should aim to explore beyond the essential tutorial readings, and you may also wish to draw upon literature from multiple weekly topics. You will find that, as you read around the literature for your chosen essay topic, different possible answers to the question emerge. There is no one correct answer; on the contrary, you are encouraged to explore different perspectives before coming to a conclusion. 3. You should aim to discuss both theoretical and ethnographic literature in your essays. Ethnographic case studies constitute anthropological “evidence”, and should be used to substantiate (i.e., to back up) your argument. It is not possible to put a precise number on this, but in a 2,500-word essay, you may want to consult 3-4 case studies (depending on the approach you choose to take). Be selective in your use of ethnography – only choose the most relevant examples that support your wider argument or point at hand. Remember, the marking criteria reward a demonstration of your ability to evaluate evidence to support an argument, analyse evidence and theories in relation to one another, and synthesise these ideas into an overarching argument. An essay that tends to just list different ethnographic examples and simply describe the details of the case studies will not score highly. 4. You should aim to advance a coherent and original argument which engages with the literature relating to your chosen essay topic. Aim to be critical in your use of the literature. The best anthropology essays critically engage with a range of relevant and well-chosen sources in constructing a compelling argument. 5. There is no single correct or standard way of structuring an anthropological essay, but the structure should be logical, and should facilitate the construction of a coherent argument. In the most basic sense, any anthropology essay should have an introduction, a main body (analysis and discussion), and a conclusion. The classic formula (attributed variously to Kant, Fichte, and Hegel) of (a) thesis, (b) antithesis, (c) synthesis is one way of structuring an essay – i.e., you present one proposition or possible answer to the question, then offer a counter-argument, before assessing to what extent both arguments are compatible in order to arrive at a new proposition. 6. In general, the writing style. for anthropology essays should be in line with the academic style. of the anthropological texts on the reading list. Please pay attention to grammar and syntax (sentence structure), and avoid writing in overly long sentences and paragraphs. Aim for clarity and concision wherever possible; brevity is beauty! 7. You can find some helpful advice and materials on the ANTH Academic Skills Moodle page and the Writing Guidelines tab in the Anth Student Hub. This includes information on how to reference in your essay, how to format your essay, as well as tips for writing style, structuring an argument, and so on. You can also make an appointment to see our departmental writing tutor at one of their drop-in sessions. 8. Please following the referencing conventions listed under the Writing Guidelines in the Anth Student Hub. In anthropology, we use the author-date in-text citation format (rather than footnotes), and all referenced sources should be listed under the title ‘References’ at the end of the document. When citing published literature, always include dates with authors’ names mentioned in the text, e.g., Durkheim (1912). Always provide page numbers with quotations or direct references to specific points or passages in the literature. 9. You must submit your coursework on Moodle using the official Anthropology Coversheet and your candidate number (available on Portico). The Coversheet can be downloaded from the Assessments tab on the Moodle site. N.b. Do not include your name on the coversheet. You can find further instructions on essay submission in the Undergraduate Handbook and under the Assessments tab on the Moodle site. 10. Remember to double check important regulations about late submissions and maximum essay word counts. 11. Finally, always make sure to thoroughly proof-read your essay before you submit it. You will lose marks for excessive typographical errors.
DEPARTMENT OF STATISTICS ANDACTUARIAL SCIENCE STAT3600 LINEAR STATISTICALANALYSIS May 16,2023 1. The data in the following table relate Yand X. It is given that, (a) Find and interpret the least squares estimates of the regression coefficients. [6 marks] (b) Construct the ANOVA table and test whether Xhas any effect on Y based on an F test at the 5%level of significance. State the hypotheses,decision rule and conclusion. [10 marks] (c) Calculate and interpret the coefficient of determination. [3 marks] (d) Calculate the sample covariance matrix of the least squares estimates of the regression coefficients. [6 marks] (e) Estimate the means of Ywhen X=0.05 and -0.05.Find a simultaneous Bonferroni confidence region for the estimation with at least 90%confidence level. [7 marks] [Total: 32 marks] 2. A regression analysis of Y on X₁ and X₂ with normal errors is considered. Fifty observations are obtained. It is given that SST is 0.205. The values of SSE for various independent variables in a model are given as follows.Conduct a forward selection method with the selection level of an F-value being 3.0.Show the steps of the selection procedure. [Total:10 marks] 3. A regression analysis of Yon X₁and X₂with normal errors is considered.The following matrices are computed. The elements of the matrices are properly ordered according to the regression function given above. (a) Find the least squares estimates of the regression coefficients. [3marks] (b) Construct an ANOVA table for the regression analysis.Test whether there is a regression between the dependent and the independent variables at the 5%level of significance.State the decision rule and conclusion. [10 marks] (c) Test the following hypothesis at the 5%level of significance, H₀:β₁+β₂=0. State the decision rule and conclusion. [5 marks] (d) Construct a 95%prediction interval for y₁+2y₂where y₁is a future response where = (0.5, 0.5) and y₂is a future response where = (-1,0.5). [6marks] [Total:24 marks] 4.A study of the effects of two factors,A and B,on an outcome Y was conducted.Factor A had three levels and Factor B had two.All six combinations of Factors A and B had the same number of observations.A two-way ANOVA model with interaction effects was employed.Part of the ANOVA table is given below. (a) Write down the two-way factor effects model for the study.Specify the model assumptions. [3marks] (b) Fill in the blanks marked by"?"in the ANOVA table. [6 marks] (c) Test at the 5%level of significance for the interaction effects between the two factors. [3marks] (d) Test at the 5%level of significance for the main effect of Factor A. [3marks] (e) The marginal means of Y for the three levels of Factor A are given in the following. Construct a 95%confidence interval for e = μ1. μ2.+μ3., where μi.is the mean for Y for level i=1,2,3 of Factor A. [8 marks] [Total:23 marks] 5. (a) Consider a linear regression model Y=Xβ+E, where Xis of dimension n×p,Y of dimension n×1 and e is a vector of n variables which have means 0 but are not necessarily independent among each other.Write down the least square estimate,β,for β in terms of Xand Y.No proof is required. [1 mark] (b) Xis partitioned as X=[X₁|X₂], β as and the least squares estimate β as where X₁is the jth column (not necessarily the first column)of X,X₂is the matrix of Xwithout the jth column,β₁is the jth regression coefficient and β₂ is the vector of the remaining regression coefficients.Let ey be the residual vector obtained by regressing Yon X₂and e₁be the residual vector obtained by regressing X₁on X₂.Consider a model ey=γe₁+ξ Prove that the least squares estimate for γ is β1. [10 marks] [Total:11 marks]
CSCI 4041 Algorithms and Data Structures - Spring 2025 Homework 5 - Greedy and Graph Algorithms Due Date: Friday, May 2, 2025 by 11:59pm. Instructions: This is an individual homework assignment. You may work together to discuss con- cepts, but the solutions must be your own work. Submit your answers on Canvas, which is linked to Gradescope (be sure to correctly map the page for each problem). This assignment is divided into two parts. Part A focuses on written problems involving greedy algorithms and graphs. Part B involves route mapping using a drone visualization. Part A - Greedy and Graph Algorithms (Written Problems) Problem H5.1: Greedy Strategy (20 points) Suppose you are given a set of n x-intervals with distinct lengths w1 , w2,..., wn , and n y-intervals with distinct heights h1 , h2,..., hn. Matching an x-interval with a y-interval will define a rectangle. For example, matching the ith x-interval and the jth y-interval gives a rectangle of width wi and height hj . You want to match each x-interval with one of the y-intervals such that the sum of the areas of the resulting rectangles is maximized. More specifically, suppose you can rearrange both lists however you want. You want to rearrange them so as to maximize Σn i=1 wi hi. Briefly describe a greedy strategy for doing this, and rigorously prove that your strategy works. Problem H5.2: Matrix-Chain Multiplication (20 points) Assume you have the following set of matrix multiplications: A1 A2 A3...An Where Ai is a matrix of dimension pi- 1 × pi defined by the dimension vector p =< p0 , p1 , ..., pn > as detailed in Section 14.2. We want to minimize the number of arithmetic operations by determining a parenthesization for matrix multiplication order. Prove or disprove each proposed greedy strategy below: (a) For each recursive step, find a pair of matrix multiplications that has the smallest number of scalar multiplications (e.g. Choose Ai Ai+ 1 where pi- 1pipi+ 1 is the smallest). Multiply these two matrices first, creating the subproblem A1...M...An , where Mpi-1 ×pi+1 = Ai Ai+ 1 . (b) For each recursive step, find a pairwise multiplication that has the largest number of scalar multiplications. Add parenthesis to create a multiplication split between those two matrices. For example, if AkAk + 1 requires the most scalar multiplications, we split A1 A2...AkAk + 1...An into (A1 A2...Ak )(Ak + 1...An ). Problem H5.3: Fastest Path (20 points) A graph of a road system is given by the following adjacency matrix of speed limits. Note: • If there is no direct route, the speed limit is 0. • Some roads are one-way, and even if they are two-way, the speed limits for different directions may differ. Answer the following questions: (a) Draw the graph. (b) The chart below describes the distances between points. Find the fastest path from the“source” node D to every other node in the graph. The fastest path is the path that takes the least amount of time to reach your destination. Record both the path and the shortest time. Briefly explain your approach and show your work. Path Shortest Time D to A: D to B: D to C: D to E: Part B - Graph Algorithms (Programming Problem) Problem H5.4: Route Mapping (40 points + 5 points possible extra credit) Figure 1 - Route mapping using a drone visualization. Instructions: You are to implement several graph algorithms and test them on a drone visualization system. Submission: Submit the search algorithms .py file to the Homework 5 - Part B Grade- scope assignment. Setup: • Download the support code from Canvas: hw5-support-code.zip • Install python dependencies: — pip3 install flask — pip3 install flask cors • Run the application — Use a terminal to cd to visualization code directory and run the visualization: * python3 app.py — Navigate to http://127.0.0.1:5000 Drone Visualization The drone visualization allows you to move a robot across the UMN. As soon as the robot moves, the drone will attempt to find the robot and move to its location. Figure 2 shows how to move the robot, Figure 3 shows a control panel where you can choose whether to view the robot or drone. You can also choose the algorithm you would like to use to move towards the robot. Three simple algorithms already exist to help you get started: • Point to Point: Moves the drone directly from one point to another. • Fly: Uses a parabolic arc to fly from one point to another. • Random Graph: Flies to a random point on the map and then follows a graph to the robot. Figure 3 shows this algorithm in action. Figure 2 - Click to move the robot and route the drone. Figure 3 - Interface for choosing the algorithms for the drone to use to find the robot. Figure 4 - The drone follows a path on a graph to find the robot. Task / Rubric The search algorithms .py file has a simple graph implementation and a set of graph al- gorithms for you to implement. You will do all your work in the search algorithms .py file. You should not need to touch the visualization code. Your task is to implement a subset of the following algorithms to reach 40 points (5 points extra credit possible): • Breadth First Search — (10 points) - breadth first(start, dest) - Breadth First Search. — (5 points) - breadth first hub(start, dest) - Every node with 5 or more neigh- bors is considered a hub. Calculate the top three hubs that are closest to both the start and destination using Euclidean distance. Use the breadth first algorithm to visit each hub before reaching the destination (visit hubs in order of decreasing distance from the destination - furthest to closest). • Depth First Search — (10 points) - depth first(start, dest) - Depth First Search. — (5 points) - depth first best(start, dest) - Perform the depth first search, but when choosing an edge to follow, always choose the edge that leads to a node that is closest to the goal (e.g. has the shortest Euclidean Distance). • Bellman Ford — (10 points) - bellman ford(start, dest) - Bellman Ford Algorithm. — (5 points) - bellman ford negative(start, dest) (5 points) - When calculating the weights of neighbors, if the neighbor node name ends with a 0, then it has a negative weight. Use Bellman Ford to find the path to the destination. If no path is found, use point to point navigation. • Dijkstra / A* — (10 points) - dijkstra(start, dest) - Dijkstra’s Algorithm. — (5 points) - astar(start, dest) - A* algorithm using Euclidean Distance as the heuristic. • Minimum Spanning Tree — (10 points) - min spanning tree(start, dest) - Calculate and follow the path on a minimum spanning tree to the destination. For each algorithm, you will take in a start point (3D point) and a destination point (3D point) to write an algorithm that returns a path (list of 3D points) on a graph for the drone to follow. You may calculate the weight of each edge using the Euclidian distance between nodes. Please look at the random graph(start, dest) function to understand how to use the graph. You can create a graph with the following line of code: graph = MapGraph(‘graph.json’). You may add / edit any structure within the search algorithms.py file including the MapGraph class Note: The start and destination points passed in are 3D points, not nodes in the graph. Therefore, for each search algorithm, you will first need to find the closest start and destination nodes in the graph. You can use the following method: graph.find closest vertex(point).
Econ 151B Exam 2A Version 1 Fall 2019 PART I. True/False. 1 point each. Circle the correct answer. The hourly marginal revenue of all workers in a particular labor market is MRP = 15-L, where L is the number of workers. If L is 8 and the wage paid to women is $6.00, then then the employer is engaging in discrimination. T F In the Lundberg and Startz model discussed in class, the relationship between wages and an employee’s “signal” was described. The wage was always on the vertical axis and the signal was always on the horizontal axis. In the model, the association between the wage and the employee’s signal is steeper when the signal is noisier. T F The wages of black workers relative to white workers have been increasing continuously since the 1964 Civil Rights Act. T F Labor market discrimination can occur even if employers are not prejudiced, but when there is prejudice then discrimination is inevitable. T F After controlling for hours of work, the female/male wage ratio is 90% T F Other things equal, in the model of employer discrimination one would predict that the observed level of labor market discrimination will fall as the supply of minority workers increases T F Racial profiling is an example of customer discrimination T F Differences in education levels can explain part of the female/male wage gap but do not explain any part of the black/white wage gap. T F Wage discrimination that is driven by customer prejudice is more likely to be observed when the good that is being bought and sold is a latte (or other coffee shop drink) than when the good that is being bought and sold is a car. T F In a competitive market, statistical discrimination will be eliminated T F 1) The diagram below shows what was happening to male labor force participation rates between 1950 and 2000. Explain how the patterns below may have contributed to the increase in black/white wage ratio in the post Civil Rights era. (3 points) 2) “Profit maximizing, competitive firms will not discriminate in the hiring of workers unless consumers exercise a preference for discrimination in market products.” (2 points) Explain why you believe that this statement is true or false. 3) The graph below depicts a competitive market for female labor with many sexist employers Based on this figure, what is the equilibrium wage ratio Wf/Wm? Note: Wfis the average wage of female workers. Wm is the average wage of male workers. (2 points) 4) Suppose that there are two groups of potential workers: group A and group C. On average, group A is more skilled than group C. An unbiased test is given to all applicants, and the test is equally good at predicting the skill levels of individuals in each group. However, the test is “noisy,” in that it is not a perfect predictor of an individual’s skill level. Therefore, the employer determines workers’ wages using information on an individual’s test score (T), and using information on the average skill level (a) of the group to which the individual belongs. The diagram below shows the relationship between wages and the test score for group A. The dotted line is a 45 degree line. a) What is the value of the test score at the point where the line for group A crosses the 45 degree line? (2 pnts) b) On the diagram above, draw the relationship between wages and the test score for group C relative to group A. Make sure that you label the relationship for each group. (2 pnts) c) Do the two lines cross? Is one steeper than the other? Explain how they compare to each other and why they look the way they do in relationship to each other. (3 pnts)
Module Code: ITS66704 (April 2025) Module Name: Advanced Programming LEARNING OUTCOME By the end of this assignment, students will be able to: • Apply problem solving skills to evaluate and solve specific problems in advanced object-oriented programming (MLO2). OBJECTIVES Develop a customer service desktop application using JavaFX as the frontend interface and Langchain4j for AI-powered conversational support. You can define the target customer relevant to your application, e.g. a student service system, technical support system, airline ticketing service system etc. The system should also provide real-time chat assistance with a focus on customer queries, product information, and issue resolution. For students opting for bonus credit, implement advanced RAG techniques including query transformation, multi-source retrieval, and re-ranking of results. Your lecturer will show you example implementation of RAG using Java in class. You can read more about Langchain4j here. REQUIREMENTS You are to design and develop a JavaFX-based Customer Service Desktop Application. The application can model any customer service context (e.g., technical support ticketing system, product returns, hotel reservation services, etc.). The number of modules covered will be up to your group to decide. A typical customer service application includes components that help manage customer interactions, support requests, issue tracking, and service delivery. Below is a list of common components grouped by functionality. Ideally each student should own and develop one module from the list below. Core Components Component Description User Management Allows registration, login, and role-based access (e.g., admin, customer, agent). Ticketing System Let customers submit support tickets; includes ticket creation, tracking, and status updates. Knowledge Base / FAQ Provides self-service resources for common issues or questions. Live Chat / Chatbot* Enables real-time interaction with agents or automated chatbot assistance. Bonus marks will be given for good implementation. Email Integration Allows communication through automated/manual email replies. CRM Integration Links with customer relationship management to retrieve customer history and profile. Due to time constraints, the other operational features (e.g., dashboard, search and filter tools, SLA management, escalation rules, assignment and routing), monitoring and reporting (e.g., analytics and reports, customer feedback tools, audit trail/logs) and support and security (role-based access control, data encryption and privacy, backup and recovery) are optional and need not be implemented. The project is split into two key deliverables: • Part 1 - Analysis and Design (100%) • Part 2 - Development and Implementation (100%) Part 1 - Analysis and Design (100%) Objective: You are required to analyze the application’s requirements for the proposed customer service application module, define its scope, and present a complete design document with interface mockups and architecture overview. Deliverables: • Requirement specifications • Use case diagrams and descriptions • UI wireframes or sketches • Class diagrams and object-oriented design overview • Description of event handling and data flow The marking scheme for Part 1 is as follows: Criteria Marks Assessment Details 1. Problem Definition & Objectives 10 Clear explanation of the purpose, target users, and customer service scope. 2. Requirements Specification 10 Functional and non-functional requirements well identified and categorized. 3. Use Case Diagram & Description 10 Proper UML use case diagram with at least 3–4 valid use cases and descriptions. 4. UI Wireframes/Sketches 10 Meaningful wireframes for all main screens, labelled and logically laid out. 5. Class Diagram & OOP Design 10 Class diagram with relevant entities, attributes, relationships, and methods. 6. Event Handling Plan 10 Explanation of expected events and handlers in key UI components. 7. Data Storage 10 Strategy for storing, retrieving, and validating data (e.g., use of lists, file I/O). 8. Architecture Overview 10 Clear explanation of MVC or any architectural pattern applied. 9. Tool Usage & Planning 10 Tools selected (JavaFX, Scene Builder, IDEs) and project timeline draft. 10. Documentation Quality 10 Neat, organized, and complete submission with proper headings and consistency. Deliverables A well-structured and properly formatted academic document that contains the detailed specifications for the proposed system, associated solution high-level design diagrams, and interface prototype diagrams. Ensure that your submission includes a cover page (page 1 of this document) which shows your group member names and student IDs. All submissions should be in pdf format (GroupNo_ProjectPartA.pdf). Part 1 Due Date: 22/06/2025 11:59pm submit via mytimes.taylors.edu.my submission link. Part 2 - Development and Implementation (100%) Objective: Based on your analysis, implement the full application using JavaFX, applying OOP principles, proper GUI design, and event handling mechanisms. Deliverables: • Complete source code (organized by packages) • JavaFX GUI application (must be runnable) • Test cases or user testing evidence (screenshots, logs) • Final user guide or README • Video demo (optional but recommended) The marking scheme for Part 2 is as follows: Criteria Marks Assessment Details 1. Functional Implementation 10 Key features described in Part 1 are implemented and working correctly. 2. GUI Design & Layout 10 Clean, user-friendly layout using JavaFX components and proper scene switching. 3. Event Handling 10 Buttons, inputs, and other UI elements respond correctly using JavaFX event handling. 4. Code Organization & OOP 10 Code modularity, use of classes, inheritance, and interface where applicable. 5. Data Handling 10 Proper handling of customer data (e.g., validation, storage, reading from file). 6. Input Validation & Error Handling 10 Input fields are validated, and exceptions are managed gracefully. 7. MVC or Architecture Use 10 Evidence of clear separation of concerns in the implementation. 8. Readability & Comments 10 Code is readable, uses naming conventions, and contains helpful comments. 9. Testing & Debugging 10 Test cases shown or described with evidence of debugging effort. 10. Completeness & Bonus Features 10 The app is fully functional with extra features (e.g., comprehensive chatbot, search, export, reports). MARKING RUBRICS For EACH criterion of marks allocated, the following rubrics will be applied: Deliverables Your pdf report along with your zipped application project folder. Ensure that your submission includes a cover page which shows your group member names and student IDs. File names should be named as follows: “GroupNo_ProjectPartBReport.pdf” – The pdf copy of your report “GroupNo_ProjectPartBProgram.zip” – The application project folder Part 2 Due Date: 13/07/2025 11:59pm submit via mytimes.taylors.edu.my submission link.
MTH 223: Mathematical Risk Theory Tutorial 1 Formula Sheet • Gamma with parameters α > 0 and β > 0 : A random variable X is said to have a gamma distribution denoted by X ∼ G(α, β) if X has the following probability density function (p.d.f): The gamma function Γ(α) is defined by with Γ(α + 1) = αΓ(α) for α > 0 and Γ(n + 1) = n! for n = 0, 1, 2, ... 1. In this course, we often encounter the computation of integrals such as dt and dt. The following formula can be used to expedite the computation: Also note that the Gamma function is defined by (a) Prove (1.1) by induction principle. (b) Using (1.1) to show Γ(n + 1) = n ! for n = 0, 1, . . .. Therefore, (c) Consider a loss r.v. X ∼ GAM(α, θ) with α = 3 and θ = 2 so that it has a pdf Compute Var(X). 2. Assume a random loss X is subject to a normal distribution with mean µ, standard deviation σ, and pdf Let φ(x) and Φ(x) respectively denote the pdf and the cdf of the stan-dard normal distribution. Show the following (a) VaRp(X) = µ + σΦ −1(p); (b) TVaRp(X) = 3. Assume a random loss X has a Pareto distribution with scale parameter θ > 0, shape parameter α > 1, and cdf F(x) = , x > 0. Find VaRp(X) and TVaRp(X) for p ∈ (0, 1). 4. Suppose that the distribution of X is continuous on (x0,∞) where −∞ < x0 < ∞ (this does not rule out the possibility that Pr (X ≤ x0) > 0 with discrete mass points at or below x0). For x > x0, let f(x) be the pdf, h(x) be the hazard rate function, and e(x) be the mean excess loss function. Demonstrate that and hence E[X | X > x] is nondecreasing in x for x > x0. 5. Assume that a stock index at the end of one year is X which has a Pareto distribution with cdf The return of a one year guaranteed investment linked to the index is Y = max{X, 100}. Denote the distribution function of the return by FY (y). (a) Calculate FY (y) for all y ∈ (−∞,∞). (b) Calculate the mean of the return. (c) Calculate the median of the return. (d) Calculate the variance of the return.
Discipline of Accounting, Governance and Regulation ACCT6029 – Case study 1 General Description This Case study 1 individual assessment is worth 15% of your final grade and is compulsory which means you must undertake this assessment, otherwise you will receive zero mark (0) for this assessment. The deadline for submitting this assignment via Canvas is 31 Mar 2025 at 23:59. You are required to submit the files via Turnitin, the university’s plagiarism software. Serious penalties may be applied if a student is found to have engaged in plagiarism. The filename of your submitted assignment should also be your SID. Please ensure you submit well before the due time in case there are problems. Turnitin in Canvas may not automatically email a digital receipt. Once you have successfully submitted your assignment, a time and date stamp will appear next to your submission. Should submission problems arise, you should contact the University’s ICT Service Desk on 02 9351 2000 or email [email protected] For the written report (Microsoft word doc) you should apply the following page set-up and formatting. Font: 12pt Times New Roman. Margins: 2.5cm in all sides. Line spacing: 1.5 lines. Length: 1500 words. Information provided in appendices/references will not be counted as words. Assessment task (30 marks - will be converted to 15%): There are four main tasks (A to D) for this Case Study 1. Task A to C will need to be completed in word doc and Task D is just uploading executed Jupyter notebook. Task A: Problem Definition: define a problem that you would like to explore with a clear motivation (why it’s important!). (6 marks) Hints: you probably want to go through WRDS first to see data availability, then define a problem. You may have a fantastic idea that you would like to explore but ifthere is no data then you can’t analyse!! You can either choose classification or regression issue. Task B: Data Collection: discuss data collection procedure and cleaning process. You will need to download and merge at least two datasets from https://wrds-www.wharton.upenn.edu/login/ (6 marks) Task C: Exploratory Data Analysis (EDA): perform. (present) and analyse/describe the EDA that you have undertaken. (15 marks) Task D: Executed Jupyter notebook with step-by-step code. (3 marks) You are required to write consciously. You can copy and paste output/images from Jupyter notebook, and follow logical orders (steps). You will need to upload both the word doc and Executed Jupyter notebook. You don’t need to provide the data at this stage, but you must save the data for two reasons: a) the marker may ask for the data ifany suspicious warrants arise and b) the same data could be used for your case study 2. Grade Descriptors: Due to the specific nature of this assignment, students are advised generic submissions will not score high marks as the assignment requirements will not be met. The following can at best be considered as a general indication, assuming consistency in quality of component parts. High Distinction The attempted solution substantially exceeds minimal requirements, and is consistent and coherent. It excels in substantive content and demonstrates professional communication skills. It reflects consideration of your own well developed perspectives and is framed in your own words. Distinction The attempted solution substantially exceeds minimal requirements across most tasks and through effective communication demonstrates a strong understanding of the issues and relevant requirements. Good development of your own perspectives. Credit The attempted solution exceeds minimal requirements across most tasks, but is less well developed, less framing in your own words (e.g. dull reproduction), some errors or omissions in application. Pass A minimal standard is reached across various aspects of the task. Fail The task is not completed. The solution does not reflect an appropriate understanding of the question/assigned task. Poorly developed responses.
INST0069 Assessment 2 Assignment Brief and Instructions 24/25 Detailed Description The goal is to develop an ontology to model a specific knowledge domain. The project consists of the following five parts: 1. Create an OWL ontology that describes the domain using Protégé . The ontology must contain definitions of the ontology classes, the object properties and the datatype properties. Use features of OWL to enrich the ontology with property types, property axioms, class axioms and property restrictions. 2. Draw a graph representation of the main classes and properties of your ontology. This should include the classes and subclass relations, the properties and subproperty relations and the domain and range restrictions of the properties. 3. Populate your ontology with concrete instances of the classes (individuals) and statements (property assertions) involving all properties of the ontology. 4. Write and execute three SPARQL queries, which are relevant to the domain. The coursework consists of two parts: • a report of a maximum of 1500 words (excluding references and figures) • an OWL file (exported from Protégé) containing the ontology code The report must contain: • a description of the scope of the ontology • a description of the classes and properties of the ontology and the features of OWL that you have used • a graph representation of the classes and properties of the ontology • the code for the three SPARQL queries and their results • examples of statements using individuals and properties of your ontology The OWL file will contain the ontology code and must be consistent with what is described in the report. To create the file in Protege, select File -> Save as and then choose the Turtle notation. Suggested Topics: You can choose to model any knowledge domain after discussing it with the module tutor. Here are some suggestions: Transportation (trains / underground / buses / flights), Museum Collections, Shop (furniture shop, supermarket, etc.), Sports, Bookings (hotel /cinema /restaurant, etc.), Calendar, Car Rental, Travel Packages, E-Commerce sites, Social Networks, Dating sites Marking Criteria Each submission will be first marked according to the criteria given below, and a sample of submissions will also be second marked, using open and check marking, in accordance with the guidelines in https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessment-framework-taught-programmes/section-4-marking-moderation#4.6. Marking Criteria Marks Requirements for maximum marks Are the overall structure and look of the report good? 0-5 The sections of the report are clear, the figures are clear and properly labeled, any external sources are correctly cited, the report is readable and complete. Does the report describe clearly the scope of ontology? 0-5 The report describes clearly the domain and potential uses of the ontology. It also explains how it can be further extended to model other domain-related concepts or other similar domains. Is the ontology graph correct? 0-5 The representation of the different elements of the ontology is correct and the classes, properties, and their relationships are meaningful. Is the ontology graph complete? 0-5 The ontology graph contains examples of subclass and subproperty relationships and captures the most important concepts of the domain. To get the full marks your ontology must contain at least 10 classes, 10 object properties and 10 datatype properties. Does the report describe clearly the ontology? 0-5 The report describes the classes and properties of the ontology, and any assumptions that were made when designing the ontology. Do the ontology and the report contain examples of different property types, inverse properties and disjoint classes? 0-5 The ontology and the report contain correct examples of at least three different property types (functional, symmetric, transitive), pairs of inverse properties and disjoint classes. Do the ontology and the report contain examples of property chains, unions/intersections of classes and different types of restrictions? 0-5 The ontology and the report contain correct examples of at least one property chain, one union and one intersection of classes and property restrictions of three different types. Do the ontology and the report contain a sufficient number of individuals and property assertions? 0-5 The ontology and the report contain examples of instances for the main classes of the ontology and property assertions involving all properties of the ontology. Does the report contain examples of SPARQL queries? 0-5 The report contains three SPARQL queries using various features of SPARQL along with their results, which are correct and meaningful. Is the OWL file correct and consistent with what is described in the report? 0-5 The OWL file can be uploaded in Protégé , is logically consistent, and contains exactly those elements that are described in the report.
Econ 151B Human Resource Economics One piece of evidence that wage convergence between blacks and whites that occurred during the 1960s and 1970s was due to something other than a decline in whites' levels of prejudice is that a. black unemployment rates fell in the south b. white women and black women were already paid nearly equal wages c. wages converged the most in the south d. survey responses indicate that white employers prejudices in the south have remained high since the 1960s Which of the following statements is false? a. white women earn more than black women at every level of education b. race differences in earnings among women are about the same as race differences in earnings among men c. controlling for education and other common measures of productivity, earnings gaps between men and women are about 10% d. earnings differences between white and black men grow with education If employers discriminate because customers are prejudiced then a. they are not maximizing their profits b. the market cannot be perfectly competitive c. they will have to charge lower prices for the goods or services they sell d. they will likely charge higher prices for the goods or services they sell Other things equal, in the model of employer discrimination one would predict that the observed level of labor market discrimination will a. increase as the supply of minority workers increases b. fall as the supply of minority workers increases c. increase as the supply of all workers falls d. remain constant as the supply of minority workers falls In their paper "Are Emily and Greg More Employable than Lakisha and Jamal?" Bertrand and Mullainathan use a research design that most closely approximates a. an in-person audit study that compares job offers between black vs. white auditors b. a resume audit that compares job offers among individuals with “black-sounding” names vs. “white-sounding” names c. a resume audit that compares interview call-back rates for interviews among individuals with “black-sounding” names vs. “white-sounding” names d. an in-person audit that compares short-list call-back rates for black vs. white auditors
MANG6134W1 SEMESTER 2 EXAMINATIONS 2017-18 RISK TAKING AND DECISION MAKING 1. A charity, FAID, which aims to alleviate poverty throughout the world, is trying to select the most appropriate strategy it should adopt in order to reduce the impact of global warming on the poorest nations. (a) Discuss whether objective or subjective probabilities might be the most useful in helping FAID select the most appropriate strategy. [30 marks] (b) Outline the advantages to FAID of using groups, including the value of prediction markets, for making judgements concerning the likelihood of events which might influence their decision. [40 marks] (c) Outline the stages in a structured methodology, employing multiple probability assessors, that FAID could employ to assess the probability that global warming will lead to a reduction in the wealth of the poorest nations over the next 30 years. [30 marks] 2. Southampton football club (S), is trying to avoid relegation, and as a result they are negotiating with Manchester City football club (M) over the price they will pay to secure M’s leading striker. (a) Describe the information S would need and the conditions which need to exist to enable them to model these negotiations as a zero sum game. [20 marks] In an effort to improve the performance of their existing players, S are also negotiating to purchase statistical analysis of their players’ performances from Perfect Performance plc (P). S and P both believe they each have three potential strategies they can adopt in these negotiations, S1, S2, and S3 and P1, P2 and P3, respectively. They both expect that the amount (£00,000) which S will be charged for the statistical analysis will depend upon the negotiation strategies adopted by both P and S, as follows: (b) Identify the two potential saddle points to these negotiations and explain how you arrived at this solution. [20 marks] (c) Discuss whether either of these potential saddle points represent a stable outcome to the negotiations. [15 marks] A senior member of S’s management team indicates that they agree with the estimated costs of purchasing the statistical analysis shown in the gain matrix shown above if S adopts S2 or S3. However, they believe the costs of purchasing the statistical analysis if S employed strategy S1 will be 8, 4 and 8 if P adopts strategies P1, P2 and P3, respectively. They also believe that S can also employ a new negotiating strategy S4, which results in S paying (£00,000): 1, 8 and 1 if P adopts strategies P1, P2 and P3, respectively. (d) If the senior member of their management team is correct, what would be P’s optimal negotiation strategy/ies and how much is S likely to pay for the statistical analysis [you can assume that there is NO saddle point solution]? [45 marks] 3. (a) Discuss why individuals use heuristics when making judgements. [15 marks] It has been found that individuals adopt the following heuristics: (i) They judge the probability that an event belongs to a particular ‘class’ by how ‘typical’ of that class it appears to be – irrespective of the base-rate probability of such an event belonging to that class. (ii) They are generally poor at adjusting probability estimates from a given starting point. (iii) They judge events more probable the more readily they can be pictured or recalled. (b) Discuss, with the aid of examples, in what way each of the findings (i-iii) can bias judgments in practice. [65 marks] (c) What steps can an organisation take to reduce the chance of these biases occurring? [20 marks] 4. The UK Government decides that, following BREXIT, it should adopt one of four possible international trade strategies (W, X, Y and Z). The Government believes that the success of these strategies should be measured in terms of the economic growth rate in the UK over the next 5 years. They also believe that the ability of each strategy to deliver economic growth depends on the average interest rates in the US over the next 5 years (low, medium and high). Consequently, they develop the following payoff matrix: Expected economic growth rate in the UK(%) The UK Government are risk averse for levels of economic growth less than 2%, risk neutral for levels of economic growth between 2% and 5% and risk preferring for levels of economic growth greater than 5%. (a) Draw the possible shape of the UK Government’s utility curve for levels of economic growth between 0 and 6%. [20 marks] (b) Discuss to what extent the information given above is sufficient for the UK Government to make an informed decision about which trade strategy to follow and suggest any additional information you think they need to collect. [30 marks] (c) Explain how the UK Government could use the utility curve you have drawn in part (a) to determine the best trade strategy for the UK Government to adopt [NO CALCULATION REQUIRED]. [30 marks] Prospect Theory suggests that in the absence of an analytical approach to the problem suggested by decision analysis, the UK Government would probably assess the value of each trading strategy by combining a subjectively weighted value of the probability they attach to the likely level of US interest rates over the next 5 years (low, medium and high) with a subjectively weighted value they attach to the different levels of economic growth they expect the UK to experience. (d) Draw a likely shape of the Government’s probability weighting function predicted by Prospect Theory and explain what this shape implies about the biased manner in which the Government might view the probabilities of different size. [20 marks] 5. Describe the normative, structured approach to decision- making suggested by decision analysis, outline its advantages and the problems of applying it in practice. [100 marks] 6. HC Holidays, rent out holiday apartments in Croatia. The number they rent out is highly dependent on the Summer temperatures. The managing director, Howard Crook, is considering five potential marketing strategies for the coming year (A-E) and he believes that the profit they will make from these rentals in the coming year will depend upon the coming Summer’s temperatures, as follows: Profit achieved by HC Holidays (a) In the absence of further information, determine which strategy HC Holidays should adopt if they employed the maximax, maximin, coefficient of optimism and regret criteria. [25 marks] (b) Discuss the advantages and disadvantages of each of the decision making under uncertainty criteria employed in (a). [25 marks] (c) What data would HC Holidays need in order to construct a risk/return diagram to compare strategies? Discuss the value of HC Holidays employing a risk/return diagram when making choices between competing strategies [NO CALCULATION REQUIRED]. [20 marks] Howard Crook decides that the chance of low temperatures being experienced in the coming Summer is zero, that the probability of high temperatures is 0.5 and the probability of medium temperatures is 0.5. (d) Under which circumstances should HC holidays employ strategy A if they used the Markowitz risk adjusted statistic for making their choice of strategies? [30 marks]
Homework Problem Set #3 P1. An epidemiologist conducted two cohort studies investigating the association between high cholesterol and development of coronary heart disease (CHD) in two different populations (A & B) where 10,000 persons were sampled (5,000 from each population). Using data in the table below, answer the following questions. Calculate the relative risk (RR) and odds ratio (OR) for the association between high cholesterol and CHD in Population A & Population B. A High 80 920 A Low 160 3840 B High 280 1120 B Low 360 3240 Q1. Which measure of association is identical in the two populations and which is not? What can account for the latter and what is the degree to which the measure overestimates the magnitude of association between high cholesterol and CHD in the two populations? (refer to equation in class) Q2. Given the study design, which of the two measures of association would you report? Also, provide an interpretation of the measure you would report. Q3. Which of the following scenarios would yield an odds ratio and relative risk that are identical? A. Common disease B. Rare disease C. Null association between exposure and outcome P2. How does the investigation of a rare versus common disease affect accuracy of the estimate of association reported in a case-control study? P3. When testing an association from a case-control study, epidemiologists often make the ‘rare disease assumption’ in interpreting the estimate. Why doesn’t the epidemiologist estimate disease prevalence from the case-control study rather than assume a rare disease? P4. Explain how we can interpret an odds ratio (OR) from a case-control study in terms of the ‘odds of developing disease’ when cases are recruited after developing disease? P5. A case-control study examining the association between hormone replacement therapy (HRT) and uterine cancer revealed that 15% of the 300 cases and 10% of the 600 controls had used HRT. Calculate the odds ratio and its 95% confidence interval, interpret the estimate and indicate whether it is statistically significant. Yes No P6. A cohort study of 10,000 participants was conducted to estimate the risk of lung cancer and coronary heart disease (CHD) attributable to cigarette smoking. Use the information below to answer the following questions. Yes No Yes No Smoker 65 1935 175 1825 Non-Smoker 17 7983 389 7611 Q1. Calculate the attributable risk (AR), the attributable fraction (AF), and relative risk (RR) for the two outcomes. Explain why AR does not show the same pattern across the two disease outcomes as AF and RR. Q2. Suppose the cohort study was conducted in California which has a smoking prevalence of 15%. Calculate the population attributable risk (PAR) and population attributable fraction (PAF) for lung cancer. Why is the PAF considerably lower than the AF? Q3. Under what conditions would the PAF be equal to AF? A. 0% of population smoked cigarettes B. 50% of population smoked cigarettes C. 100% of population smoked cigarettes D. smoking prevalence in the sample = smoking prevalence in the population P7. The following table provides descriptions of four different studies and how they were sampled to estimate the association between Exposure X and Disease Y. Answer the following questions based on the descriptions. Study Description of Sample A Selected a random sample of residents from the Orange County population (n=5000 participants) B Selected all participants with Disease Y and a subset of participants without Disease Y from Study A (n=500 participants) C Selected all participants without Disease Y from Study A to be followed for five years (n= 4750 participants) D Selected all participants who developed Disease Y and a subset of participants who did not develop Disease Y from Study C (n= 400 participants) Q1. Identify which study (A, B, C, D) corresponds to which study design in the two-dimensional classification system discussed in class Q2. What measure of morbidity can only be estimated from Study A, and why? Q3. What is the primary advantage of over-sampling participants on the disease outcome in study B versus study A, for example? P8. Explain why cross-sectional (prevalence) studies that utilize probability sampling tend to have great external validity but poor internal validity. P9. An epidemiologist tested the association between use of NSAIDS (anti-inflammatory medications) and colorectal cancer based on a hospital case-control study in which cases were admitted for colorectal cancer and controls were admitted for arthritis. Q1. Does selecting cases and controls from the same hospital ensure that study participants came from the same source population? Why/why not? Q2. What fundamental principle was violated in selecting controls with arthritis, and how did this violation affect the measure of association between use of NSAIDs and colorectal cancer?
Term Project: Analyzing Euclid Space Telescope Images Project Overview One of the biggest mysteries in astronomy is how galaxies form. and develop over time. The James Web Space Telescope (JWST; launched in late 2021) was designed in part to solve this mystery by peering back near the beginning of time, to the era when modern galaxies were just forming. As you may have read in the news, the results from JWST have provoked significant controversy, as they appeared to challenge our standard thinking about how galaxies form. More recent work has revised that assessment, bringing the JWST results more in line with existing theories (for example, see: https://science.nasa.gov/missions/webb/webb-finds-early-galaxies-werent-too-big-for-their-britches-after-all/ (https://science.nasa.gov/missions/webb/webb-finds-early-galaxies-werent-too-big-for-their-britches-after-all/) ). So, there are still many mysteries about galaxy formation, some of which can be explored by carefully scrutinizing images of galaxies across cosmic time. That's a big part of what you'll be doing in this project. JWST is a very powerful telescope but, like all telescopes, it's optimized for a particular kind of science. JWST is designed to provide extremely detailed images of very small portions of the sky. This is useful if you want to study a small number of objects in unprecedented detail. But it's less useful if you need to study huge numbers of objects. To understand galaxies generally, we need to examine millions or billions of them. You can think of this a little like the difference between psychology and sociology: psychologists analyze the minds of individual humans, while sociologists look for patterns in the behavior. of many humans. If JWST is a galaxy psychologist, the European Space Agency's Euclid Space Telescope is a galaxy sociologist. Launched in 2023, Euclid (https://www.esa.int/Science_Exploration/Space_Science/Euclid (https://www.esa.int/Science_Exploration/Space_Science/Euclid) ) was designed to map billions of galaxies across the universe. One of its goals is to build a map that will allow us to understand how dark energy has shaped the cosmos over time. To do this, Euclid is optimized in a very different way from JWST. JWST has a larger mirror that it uses to observe the universe at infrared wavelengths. Infrared wavelengths are very useful if you want to see light from the early ages of the cosmos. Euclid has a smaller mirror and it gathers visible and near-infrared light. Euclid's smaller mirror doesn't allow it to see each individual galaxy in as much detail as JWST, or to look as far back in time, but it can see vastly more galaxies over a long stretch of the history of the universe. When we try to unravel the history of galaxies, we face a hard physical limit: galaxies evolve over billions of years, which is far longer than a human lifetime. Our photos of the cosmos show galaxies in "freeze-frame", at some random moment in their extraordinarily long lifespans. this means we can't actually watch one type of galaxy evolve and change into another. We can't see the stars in a galaxy flare to life for the first time, then dwindle as the galaxy runs out of gas to form. new stars. Here a biological analogy is helpful. A developmental botanist might take individual seeds and study them as they germinate, grow to seedlings, flower, and ultimately die. But a forest ecologist can't watch the entire lifecycle of a forest. Instead of following a single plant for a long period of time, she might hike through a forest, taking photos and measurements of everything she sees, later assembling it into a consistent story of what is going on in that forest. In this project, you're going to become galactic ecologists. Each of you will be assigned a high-resolution snapshot of a segment of the sky recently taken by the Euclid space telescope. Your goal will be to analyze the photo to learn everything you can about galaxies, their formation, and their evolution. Because this project relates to content that won't be covered until later in the course, we recommend that you don't finish it until closer to the end of the semester. However, you should look it over early in the semester so you have an idea what's involved, what you should be focusing on learning in class, and the overall time commitment. Accessing Your Image Every student is provided TWO unique images to work on. To earn grades, you must choose ONE AND ONLY ONE of your own personalized images. Make sure to state in your report which of your images you are choosing. It's totally up to you to decide which image to analyze. The images are generated randomly, so we provide two in case you find one of them too tricky to analyze. But in the end, you must only analyze ONE of them. You will find your two images in this directory, labeled with your utorid: AST201_winter2025_student_Euclid_images (https://utoronto.sharepoint.com/:f:/s/ArtSci-AST/adminsite/EhsXh0aLF0pHl3zBvzOBbasBSccmCO-xvQ-Yfl1SCBF-Bg?e=GbaMUP) If you registered for this project late, your personalized image might not appear here. If that's the case, please contact us at the email address given in the syllabus (https://q.utoronto.ca/courses/377762/assignments/syllabus) . Understanding Your Image When interpreting your image, it's crucial you keep several factors in mind. The objects in your image are probably spread out over billions of years of cosmic time. Astronomical images capture whatever objects happen to lie along the line of sight, each potentially at a very different distance from the others. This means that they may also be at very different points in time. Two objects that appear side-by-side in an image might be separated by billions of years in time. That is, they might have very different "lookback times". This video does a great job of explaining this concept: https://www.youtube.com/watch?v=yfWYXY85mBk (https://www.youtube.com/watch? v=yfWYXY85mBk) (https://www.youtube.com/watch?v=yfWYXY85mBk) Don't confuse brightness and distance You can't necessarily assume that something is distant because it is faint. An object that appears faint in your image may be a very bright object seen from a very great distance, or it may actually be a very faint object seen from up close. That means you might see a little fuzzy patch in your image and be unsure whether it is a distant galaxy halfway across the universe or some little cloud of gas within our own Milky Way galaxy. This is a genuine ambiguity you will have to grapple with, and should factor into your interpretations. Your image is made of visible and near-infrared light Your image is a 1000 pixel by 1000 pixel PNG file. Each image is composed of both visible and near-infrared light. That means that the colors are NOT true to the colors the human eye would see. You will need to take this into account in your analysis. Here is how the Euclid team describe the color-coding of these images: "The blue, green, red channels capture the Universe seen by Euclid around the wavelength 0.7, 1.1, and 1.7 micron respectively. This gives Euclid a distinctive colour palette: hot stars have a white-blue hue, excited hydrogen gas appears in the blue channel, and regions rich in dust and molecular gas have a clear red hue. Distant redshifted background galaxies appear very red. In the image, the stars have six prominent spikes due to how light interacts with the optical system of the telescope in the process of diffraction. Another signature of Euclid's special optics is the presence of a few, very faint and small round regions of a fuzzy blue colour. These are normal artefacts of complex optical systems, so-called ‘optical ghost’; easily identifiable during data analysis, they do not cause any problem for the science goals." For reference, your eye would perceive light with a wavelength of 0.7 microns as red. Your eye cannot perceive light of 1.1 or 1.7 microns, as these are both infrared. That makes these "false color" images. The Euclid team have followed the general astronomical convention of color coding the shortest wavelength as blue, and longer wavelengths as red (as is the case for wavelengths in the visible part of the spectrum). Correctly interpreting what the colors mean will be crucial to your analysis. Each image covers a patch of sky about 0.2 degrees on a side, which means the whole image is around 20% the size of the full Moon on the sky. There are certain well-understood "artifacts" in your images The design of a telescope affects the appearance of objects it photographs. You must be aware of this to correctly interpret what you are seeing. "Diffraction spikes" are a very common type of visual artifact that is entirely produced by the telescope itself. They typically appear around the brightest objects in the image and they look like this: Those six bright spikes emanating from the star are caused by the structure of the telescope. You should not mistake them for celestial phenomena. Similarly, the fact that the star appears as a little circle instead of a single-pixel dot is also an effect of the telescope. Stars are "point sources" to Euclid, meaning that the telescope isn't actually capable of "resolving" the surface of the star. The round shape of the star in the image is caused by an intrinsic "blur" present in all astronomical images to differing degrees. There are other known image artifacts in the Euclid data. You should do some reading about the telescope to inform. your interpretation of your images. Don't misinterpret data artifacts as real physical objects. What to Do With Your Image Your main task is to very carefully scrutinize your image. You should be looking for evidence about the structure and evolution of galaxies in the universe, while carefully screening out unrelated objects and image artifacts. You should try to identify at least one of each of the following types of objects, and always include the BEST candidates for each category: 1. A star that you believe is in the nearby universe (e.g. the Milky Way) 2. A spiral galaxy 3. An elliptical galaxy 4. A galaxy whose type is ambiguous. 5. Two galaxies of the same type that you believe are being seen at very different points in time (that is, separated by billions of years in time) 6. Two or more galaxies that appear to be physically interacting with one another (e.g. in the process of colliding or merging). 7. Gravitationally lensed galaxies 8. At least one anomalous celestial object you can’t identify, with an evidence-based hypothesis as to what it might be. This should NOT be an image defect (including any of the ones described in the previous section). 9. An estimate (or count) of the total number of galaxies For each of the above (except the last), you should include the following: 1. One or more images. If you want to present more than one image from the same category in an efficient way please consider making a small mosaic, like this one: 2. A brief description of the object and your rationale for selecting it as an example of the category. 3. A clear and definitive assessment of your confidence that the object is what you have claimed it is. For example, you might say "I'm very confident that this is a spiral galaxy because the spiral pattern of its arms is unambiguous" or "I'm moderately confident that this is an elliptical galaxy, given its shape and colour, but it could also be ______ or ______." It's possible you might not find objects in one or more of these categories in your image. If you believe they are entirely absent, you should say so clearly. If you believe that they might be present but you can't be sure, include them as described above, but with a low confidence assessment. For full marks, your answers to the above should be informed by additional research concerning the telescope itself, the interpretation of the images, and the general topic of galaxy types and evolution. What to Hand In Hand in a report formatted as a single PDF file, no more than 5 pages in length. Your report should have these components: 1. A very brief introduction (one paragraph or less). 2. Answers to the questions in the previous section (identification of different types of objects, plus an estimate of the total number of galaxies in the image). 3. A bibliography listing all of the sources you used for your background research. You MAY cite the course textbook, but you MAY NOT cite the lecture notes from this or any other course. All of your sources should be current, written by an identifiable expert in the subject area, and preferably either edited or peer-reviewed by someone other than the author. You must include inline citations for EVERY fact, concept, or image derived from external sources, as well as a bibliography of your sources. Your bibliography can be in any common format (APA, MLA, Chicago, etc.), but all online sources MUST include a clickable link to the source, even if that is not required by your bibliography style. guide. All images should have brief descriptive captions, including a figure number (Fig. 1, Fig. 2, etc.). If you choose to use images from an external source, they must be clearly marked as such. Where there is any possibility of ambiguity, you are encouraged to annotate your images to draw attention to specific details. For example, if you were to show the following image and refer in your description to "the spiral galaxy on the left side of the image", we would consider this ambiguous and you would potentially lose marks: A better strategy would be to annotate the image as shown below and then refer to "the galaxy circled in red".
Department of Computer Science COMP2120 Computer Organization Assignment 5 Due Date : 11pm, 8th May 2025 1. Consider a Serial Interface (e.g. Modem), containing a Control & Status Register and two Buffer Registers, Input and Output Buffer Register, residing in memory location SCSR, SBRI and SBRO, The SCSR has the following format: Bit 0 =1 if Device Error Bit 1 =1 if Device Ready Bit 2 =0 if next operation is Write, 1, Read Bit 3-5 =000 if speed = 4800 bps =001 if speed = 9600 bps =010 if speed = 19200 bps =011 if speed = 57600 bps =100 if spped = 115200 bps Bit 6 =0 if odd parity, 1 if even parity Write an assembly program, using any instructions set (you may invent your own instructions) to output an array of 10 characters by Program I/O, to the serial port, using a speed of 115200 bps and even parity. To simplify the problem, you may assume that the array of characters is stored in memory location LINE, with one character in one word. Only source program is needed. 2. Given the data path of a CPU as in Assignment 4 with the modification that the MBR provides data to both S1-Bus and S2-Bus. Consider another instruction set, which allows memory operands, and the addressing mode information is stored in the same byte as the register operand. Describe the data transfer/transformation for the following 2-word instruction: ADD OFF(R1), R2, R3 which will get the first operand from memory whose address is given by OFF+R1 (dis- placement addressing mode), add it to R2 and put the result in R3. OFF is stored in the word following the instruction: ADD R1(disp mode) R2 R3 OFF
STAT3600 LINEAR STATISTICAL ANALYSIS May 20, 2024 1. Five observations of weight-adjusted waist index (X) and total bone mineral density (Y) are given as follows. Consider a linear regression model when total bone mineral density is regressed on weight-adjusted waist index. It is given that (a) Calculate the least squares estimates of the intercept and the slope. Interpret the estimates quantitatively. [6 marks] (b) Calculate the standard errors of the estimated intercept and slope. [6 marks| (c) Construct a 95% confidence interval for each parameter. [4 marks] (d) Test at the 5% level of significance whether the slope is -1. [1 mark] (e) Estimate the mean for total bone mineral density when weight-adjusted waist index = 9.8. Construct a 95% confidence interval for the estimate. [4 marks| [Total: 21 marks] 2. A study is conducted to investigate how daily caffeine intake affects the risk of depression in both the cancer and noncancer populations. A depression scale PHQ-9 is used to measure the severity of depression of the subjects. The caffeine intake is categorized as four levels. The data are given as follows. (a) Calculate and plot the means for PHQ-9 for the six treatments. Does it appear interaction effects between cancer status and Caffeine intake? Explain. [6 marks] (b) Estimate the main effects of Cancer, main effects of Caffeine intake and the effects of interaction. [6 marks| (c) Complete the following two-way ANOVA table. [6 marks] (d) Test whether or not main effects for Cancer are present, using a 5% significance level. [3 marks] (e) Compare the mean PHQ-9 between the two cancer groups for each of the three Caffeine intake quartiles by constructing the at least 95% simultaneous confidence intervals using Bonferroni’s method for the three quartiles. Hence, describe the difference between the depression of cancer and noncancer patients. [7 marks| [Total: 28 marks] 3. A general linear model is employed to study the effects of a 2-level factor A, a continuous regressor x and their interaction on a response variable Y. ‘The model is given as follows. subject to The data are summaized as follows. The matrices are properly arranged according to the above model. (a) Write down the fitted regression model. [4 marks] (b) Complete the following ANOVA table. [6 marks] (c) Test the interaction effects at the 5% level of significance. [4 marks] (d) Consider a model without the interaction term. i. Write down the fitted regression model. [3 marks] ii. Test the main effects of factor A at the 5% level of significance. [4 marks] [Total: 21 marks] 4. Consider a regression analysis of Y on X1 — X3 for 30 observations. The SSE for various sub-models are given below. (a) Determine the subset of variables that is selected by the backward elimination method, based on the removal level F = 4. Show your steps. Report the MSE and F-value at each step. Report the selected regressors. [7 marks] (b) Among the four subsets of variables, determine the subset that is selected by Cp. Produce a Cp plot. Report the selected subset. [7 marks] [Total: 14 marks] 5. Two regression lines are given as follows. (a) Show that the F-statistic for testing can be put in the form. [5 marks] (b) Obtain SSE, the error sum of squares under H,, and SS Eypo, the error sum of squares under Hp. [6 marks] (c) Show that [5 marks| [Total: 16 marks]
Department of Computer Science COMP2120 Computer Organization Assignment 4 Due Date: Apr 30, 11pm, 2025. Figure 1: A simplified CPU This assignment is based on the CPU and simulator in Assignment 2. In this assignment, extra instructions are added. They are the PUSH, POP, CALL and RET instruction. In order to implement these instructions, the CPU is modified as follows: 1. A new register (SP, the stack pointer) is included. SP provides output to S1-bus, and receives input from D-bus. Also, the SP has special hardware to increase and decrease its value by 4 (similar to PC). This is provided by the special function do incSP(), and do decSP(), which is in turn controlled by the flag incSP and decSP. 2. A new register (TEMP) is included, which is directly connected to the MAR only, via a dedicated data path. Again you can move data between MAR and TEMP and special function do MAR to TEMP() and do TEMP to MAR() are provided, which are controlled by the MAR to TEMP and TEMP to MAR flag. 3. A new flag push pop is included, which will move the SP to MAR. Otherwise, the CPU remains the same. New instructions provided include: PUSH Rn : SP ← SP-4; mem[SP] ← Rn 00001010 n 00000000 00000000 POP Rn : Rn ← mem[SP]; SP ← SP+4 00001011 00000000 00000000 n CALL proc : 00001100 00000000 11111111 00000000 RET : 00001101 00000000 00000000 00000000 Summary Opcode: Instruction Opcode Instruction Opcode Instruction Opcode ADD 00000000 MOV 00000101 PUSH 00001010 SUB 00000001 LD 00000110 POP 00001011 NOT 00000010 ST 00000111 CALL 00001100 AND 00000011 Bcc 00001000 RET 00001101 OR 00000100 HLT 00001001 The program The revised simulator program is given in sim2. py. Study the simulator code carefully. 1. Hand assemble the following assembly code and put it in a program file. Run the simulator on this program. Explain what the function SQ does? /* Procedure to calculate , input is R10, output is R11 */ /* The proc uses R12 and R13, need to save them on entry */ /* and restore them when exit*/ 2. Run the simulator in debug mode. Write down the data transfer/transformation sequences involved in the execution of the instructions CALL and RET. You may skip intermediate step provided by the simulator, for example the in- struction fetchs step should look like: MAR
Assessment brief Module code & title SOEE2650: GIS for Geoscientists Assignment title Locating a wind farm Assignment type Computer exercise Learning outcomes assessed We are assessing: · That you can add data to GIS successfully: preparing and projecting it to appear on one map. · That you can carry out basic analysis tasks, e.g. generating viewsheds. · That you can apply techniques from the workbook to new tasks and data. · That you can use GIS layout tools to present your data on a map layout in a professional manner. Assignment length/Time limit guidance Your submission should be contained on a single A3 page. Use of GenAI in this assessment AMBER: AI tools can be used in an assistive role You are permitted to use AI tools for specific defined processes within the assessment. See below for further details. Weighting 40% of the final module mark. Deadline or date of assessment 14:00, Thursday 13th March 2025 Submission method Work should be submitted to the submission point in Minerva as a PDF document. Feedback provision Usually, you will receive your feedback before your next assessment for the module is due. Where it is appropriate to do so, and feedback can be released without invalidating the integrity of ongoing assessments, this will typically be no later than 15 working days post submission. Feedback will be provided via Minerva – find it by logging back into the submission point once you have been told it is available. Module manager Clare Gordon Assignment summary guidance The UK is highly populous and siting wind farms can be a challenge. This exercise produces a series of maps with possible supporting data. The exercise is highly simplified and only considers topography and mean windspeed. You will submit a layout containing a series of six maps. The first three will show average windspeed across the whole UK. The second three will show viewshed maps of an area centred on Boston Spa in Yorkshire. More detailed information will be provided as a presentation during one of the lectures for this module and uploaded to the Assessment and Feedback folder in Minerva. Recommendations for data to use will be given in the Assessment and Feedback folder in Minerva. Use of GenAI Under this category, AI tools can be used in this assessment in an assistive role for the specifically defined processes: · acting as a support tutor to aid in the research of a topic. · providing assistance in the use of specific tools within QGIS and aiding with troubleshooting. In this assessment, AI tools cannot be used to: · produce the entirety of, or sections of, a piece of work that you submit for assessment beyond that which is outlined above. The use of Gen AI must be acknowledged in an ‘Acknowledgements’ section of any piece of academic work where it has been used as a functional tool to assist in the process of creating academic work. If it is suspected that you have used a Gen AI tool to produce part of your work, but you have not acknowledged this use, this could be investigated under the Academic Misconduct procedure. The minimum requirement to include in acknowledgement: · Name and version of the generative AI system used eg ChatGPT-4.0 · Publisher (company that made the AI system) eg OpenAI · URL of the AI system · Brief description (single sentence) of context in which the tool was used. For example: “I acknowledge the use of ChatGPT-3.5 (Open AI, https://chat.openai.com/) to summarise my initial notes and to proofread my final draft.” For further information on Gen AI and University policy on its use in assignments, see the guidance document for taught students: https://generative-ai.leeds.ac.uk/uol-genai-guidance-for-taught-students/ General guidance skills@library hosts useful guidance on academic skills including specific guidance on academic /writing and referencing Academic skills | Library | University of Leeds Assessment criteria and process The approximate weighting of marks for this assessment will be as follows: · Importing of data = 25% · Processing of data = 30% · Understanding and description of data = 20% · Presentation = 25% The assessment processes for the School of Earth and Environment can be found in the Code of Practice on Assessment (CoPA) at this link. Any criteria specific to this assessment that differ from those presented in the CoPA are detailed below and will always overrule the information published in the CoPA. The detailed assessment criteria for this assessment are provided in Minerva under Assessment and Feedback. This assignment does not assess your written English although feedback will be given. Resits: Resits will be a very similar format but applied to a different geographic location. Presentation/Formatting and referencing Please submit your work as a PDF document basing your presentation on the principles of cartography discussed in the lectures for this module. You must appropriately cite all supporting evidence using the Leeds Harvard referencing style. - https://library.leeds.ac.uk/info/1402/referencing/50/leeds-harvard-introduction
Learning Objectives Objectives To practice with: . structs . binary file I/O . data representations . dynamic memory allocation . random numbers . makefile . development tools `gdb`, `valgrind`, `git` For this project, you will be working with images in a PPM formt (described below). You will need to view images directly on the ugrad servers, which requires a few extra steps: 1. Set up X-tunnelling by installing XMING on Windows or XQuartz on Mac (Reach out to us if you need help to set this up). 2. Once you have the appropriate program running, enable X-tunnelling when you connect to the ugrad server: use -Y on a Mac when you `ssh` into ugrad or with Putty, simply enable the X-11 forwarding option before connecting. 3. `feh` is a very simple command-line image viewer. It is available on ugrad machines and you can simply run the program with the name of an image file as a command-line argument, and it will [slowly] display the image on your screen. e.g. $ feh myimage.ppm Please note that if you are using emacs while X-tunnelling is enabled, you will have to run it with the command `emacs -nw` to still run it in the terminal ("nw" stands for "no window".) Other in-terminal editors may have similar options. Note: Before connecting to ugrad and running `feh`, make sure either Xming or Xquartz is running and x-tunneling is enabled as described above. If you are using a different platform, you are welcome to use an image viewer of your choice; feh is easy to install using most linux package managers, but there are other open source image viewing programs, as well as alternatives for Windows and MacOSx. Program Description This program will be an image processing program, in the vein of Photoshop. It will have a command - line-based user interface (UI), so there will be no graphical interface, and the range of operations will be limited, but the algorithms you will use are similar to the ones used in programs like Photoshop or GIMP. At a basic level, your program will be able to read image files from disk (ie, the file system), perform one of a variety of image processing tasks, and then write the result back to disk as a new image file. Since your program will not have a GUI, you will use external programs to view the images. If you are on ugrad (either locally, or remotely with X-tunnelling), you can use the program feh. While there are many formats for storing image files, your program will only need to read and write one, the PPM format. This is essentially the simplest and easiest format to read and write, which is why it was chosen; its main drawback is that it does not use any kind of compression, so images stored in this format tend to be on the large side when compared to formats like JPEG or PNG or GIF. An implementation to read PPM files is provided for you. However, you will need to write the corresponding function to write to a PPM file format. (See ppm_io.h and ppm_io.c in the starter code.) Starter Files Make sure to do a `git pull` on the public repo before starting to work to get the starter files for this project. You must work with the starter files! PPM image format For this assignment, we will use a very simple image-file format called PPM. It’s an uncompressed format, meaning that the images will take up a lot of disk space (compared to JPG or PNG files), but it’s very easy to read and write from C code (which is why we’re using it). For the formal “official” description of the PPM format, see the netpbm site. Because these PPM files can be very large, be careful not to fill up your ugrad storage quota with too many cat pictures … 'convert' Command NOTE: you can use a unix program called `convert` to convert between image formats; e.g. to convert an existing file called "selfie.jpg" into a PPM, you would type: $ convert selfie.jpg selfie.ppm This works for most image format file extensions; it converts to/from most known image formats, including .jpg, .gif, .png, .ppm, .tiff, and .pdf, and is installed on the ugrad machines. If it's not installed on your local machine (or virtual machine), most linux package managers can install it (or can install ImageMagick, which is the suite of tools that `convert` is part of). The PPM format itself is pretty simple (compared to most other image formats). Basically, at the top of the file will be a special “tag” that marks the file as a PPM; this should be P6. Then, there are three numbers, separated by whitespace; these numbers represent the size of the image as columns, rows, and colors. Columns and rows specify the width and height of the image (in pixels) respectively. (BEWARE: columns come before rows in this format!) The colors value encodes how many different shades of each color a pixel can take on; for this assignment, this number must always be 255 (you must reject any image that uses a different value, but you’re unlikely to encounter one). Immediately after the 255, the binary data encoding all the image pixels will begin. Optionally, there may be lines starting with a #, which are comments and should be ignored; these may be intermixed with the above information. You don’t need to store these; if you read a file and then re-write it, it’s fine if the comments get lost. The files we test your code with, however, will have either 0 or 1 comment lines just after the P6 tag, but no comment lines between the other header values (see trees.ppm in the course public repo for an example). All of this will be ANSI text, so you can use the normal text I/O functions (e.g. fgetc(), fscanf(), fprintf() etc.) to read/write the header information. After the color size specification, there will be a single whitespace character (usually a newline, but that’s not guaranteed), after which the remainder of the file will be the actual pixel values. Basically, each “pixel” consists of three values; the first value is the “red” channel, the second value is the “green” channel, and the third value is the “blue” value. Taken together, these three values specify a single color, which is the RGB color value of that pixel. Since the max color value is 255, each of these values will be in the range 0-255, which fits exactly in one byte of memory. For more information about RGB color codes see Wikipedia. The easiest way to read the pixel values is to create a struct that contains three unsigned char variables, one per color channel. Then, create an array of your pixel structs with rows * cols elements. At that point, you can just use fread() to read the entire array of pixels from the file in one go. Similarly, you can use fwrite() to write the whole pixel array with a single function call. We’ve started this off for you in the provided ppm_io.h and ppm_io.c files. Your first coding task for this project is to write a few of the functions in the ppm_io.c implememtation file: . write_ppm - function to write from an Image variable to an external file in the PPM format . make_image - function to allocate memory for an Image of a specified size . free_image - function to free the dynamically allocated memory for an Image We have provided the implememtation of read_ppm (function to read a PPM formatted file into an Image), along with the struct definitions this project must use. Operational Overview Your program will be a command line tool, always run with the name of the executable file project followed by (minimally) the name of an input PPM file, the name of a desired output PPM file, and the specific lower-case name of an image processing operation, as listed below. Some operations require additional arguments, which will also be supplied at the command line by the user, at the end of the line. There are no prompts and no input entered by the user interactively. First Two Commandline Args Regardless of the desired operation, the first two arguments after the executable name project are always interpreted as the input file name followed by the output file name. The next argument is always interpreted as the operation name, and the operation's arguments (if any) come after that. The operations your program will be able to recognize and perform are all of the following, listed roughly in easiest to hardest implementation. The bolded words are the operation names to be entered at the command line by the user. More detail for each operation is provided below. 1. invert - invert the colors (i.e. black becomes white, white becomes black, etc.) 2. crop - crop the input image given corner pixel locations 3. zoom_out - zoom out on an image 4. binarize - convert the input image to black and white by thresholding 5. pointilism - apply a pointilist filter to the input 6. blur - blur the image using a Gaussian filter with a prescribed standard deviation sigma For example, at the command prompt, a user of your program might type: $ ./project building.ppm building_crop.ppm crop 50 50 500 500 to crop the input image building.ppm (in PPM format) and output the cropped image to building_crop.ppm, where (50, 50) and (500, 500) specify the top-left and bottom-right pixel locations of the cropped region. For another example, at the command prompt, a user of your program might type: $ ./project trees.ppm trees_blur.ppm blur 3 to blur the input image trees.ppm (in PPM format) and output the blurred image to trees_blur.ppm, where 3 specifies the standard deviation of the Gaussian filter. Once you implement the missing ppm_io.c functions, you can then checkout the provided demo program checkerboard. Compile the demo program by running make checkerboard; an executable checkerboard should be generated. The program demonstrates how to use ppm_io to read and write PPM formated files, and also shows how the struct Pixel and struct Image are used. Compare In the starter code, we also provide a helper executable `img_cmp`, which you could run on ugrad machine to compare if two PPM files are the same up to a tolerance. It's usage is: $ ./img_cmp PPM_file1 PPM_file2 [tolerance = 0] The program takes two PPM files with the same dimension and compares them pixel by pixel. It counts how many pairs of pixels are within the given tolerance. A pair of pixels (with same row and col indices) of two images is said within the tolerance if each absolute difference of their three channel values is less than or equal to the tolerance. For example, if you run: $ ./img_cmp checkerboard1.ppm checkerboard2.ppm 5 it shou|d te|| you how many pair of pixe|s in 、checkerboard1.ppm、and 、checkerboard2.ppm、have an abso|ute difference of more than 5 (i.e. intensity difference). After you have reviewed checkerboard.c and ppm_io.h, and comp|eted the ppm_io.c fi|e, as an initia| test to be sure you’re on the right track, try to read in a PPM fi|e and write it out unchanged. Use the img_cmp program to verify the two fi|es are exact|y the same (to|erance 0). Once this works we||, begin successive|y working through the operationa| commands as |isted. Scaffolding Folder The scaffo|ding (i.e. starter code) fo|der for this project (avai|ab|e in the pub|ic repository) provides you with ppm_io.c, ppm_io.h, checkerboard.c, project.c, img_cmp.c, and a Makefi|e for the project. It a|so contains some testing PPM fi|es in a fo|der named data and some expected resu|ts in a subfo|der named resu|ts, which is in the PPM format. Last|y, we provide starter fi|es image_manip.h and image_manip.c which is where your imp|ementations of the various transformation operations shou|d be added. Note Note that the resu|ts disp|aying on this page are png versions. You shou|d use the provided PPM ones for comparison. We encourage you to store the provided PPM images and a|| created images in a subfo|der of your own repository named data, to keep your images separate from your source code fi|es. You don’t need to submit any PPM fi|es to us; keeping them in a separate fo|der wi|| he|p you avoid accidenta||y inc|uding them. Tip If you're using the、data、subfo|der, we suggest you to execute your code from within the、data、fo|der by typing ../project, so you can refer to input fi|enames whi|e the program is executing direct|y as 、kitten.ppm、, rather than、data/kitten.ppm、, saving yourse|f the extra typing whi|e testing. Implementation Details This section contains detai|ed descriptions of the image processing operations that you wi|| imp|ement for this assignment. We use the fo||owing samp|e images for a|| the a|| examp|es/operations i||ustrated be|ow. Invert Inverting color values is very straightforward; simply take the value of each component of each pixel, and calculate its “inverse” by subtracting its value from 255. If you apply the invert transform to the kitten.ppm and trees.ppm images, the results should be as shown below. If you invert that resulting photo, you should get the original photo back. Crop Cropping an image is pretty common. For this operation the user must specify the two corners of the section they want to crop (ie, keep) - one inclusive and one exclusive. That will mean 4 integer values: the column and then row of the upper-left corner (both inclusive values), and the column and then row of the lower-right corner (both exclusive values). By looking at the differences between those values, you can calculate the size of the new image; this will let you allocate the correct amount of space for the pixel array. Once you’ve done that, you can just use a loop to go through the pixels of the specified region in the original image, and copy each component of each pixel to the new image. You should check whether exactly 4 additional arguments are provided for the cropping operation, and check if the specified corners are senseless or not. You should report appropriate errors. If you crop the kitten.ppm image from (top col=200, top row=200) to (bottom col=300, bottom row=300), the result should have 100 rows and 100 columns and look like: Binarize To binarize an image into a black and white format, we use thresholding. Therefore, this operation will take an additional input parameter as a (threshold), which is expected to be an integer and in the range between (0) and (255) inclusively. In your program, you should check if there is exactly one parameter provided for the binarize operation. Otherwise, you should report an error. You also need to check if the input (threshold) is an integer or not, and check if it is a valid number between (0) and (255). If not, you should report an error. To implement this operation, you will need to first convert each pixel to a grayscale version using the provided pixel_to_gray helper function. Then, you can calculate a single (binary) value by comparing the (grayscale) value with the (threshold) value. The (binary) value is set to (0) if the (grayscale) value is smaller than the threshold. Otherwise, it is set to (255). For each pixel, assign the same (binary) value to all three color channels of your output image. For example, if you run the below command: $ ./project kitten.ppm kitten-binarize-127.ppm binarize 127 the result should look like: pmbinarize200theresult shouldlooklike:Thebinarizedkittenima Zoom_out To keep things straightforward, you will only implement a single zoom out scale. In order to perform a zoom out, we take a 2X2 square of pixels in the input image and average each of the three color channels of the four pixels to make a single pixel. This means a zoomed out picture has half as many rows and half as many columns as the original image. However, note that the number of rows and/or columns in the input image might be odd. In this case we will simply discard the data in the odd bottom row and/or odd rightmost column. ./project kitten.ppm kitten_zoom_out.ppm zoom_out will result in: projecttreesppmtreeszoomoutppmzoomoutwillresultin:The zoomedout treesimagePointilismappliedon treesimage
1. Introduction to Time Series ECO374H1 Department of Economics Summer 2025 Forecaster's Objective For illustration, see the code file 1. ACF and PACF (section 1) Features of Time Series Moving Average Smoothing Moving Average Smoothing (MAS) of order m: where m = 2k + 1 Note that each data point has the same weight m/1 MAS is typically used for "seasonal adjustment" of data, i.e. filtering out seasonal variation to estimate the trend-cycle component Simple Exponential Smoothing Simple Exponential Smoothing (SES) assigns the most recent observation the most weight, and the most distant (in time) observation the least weight Denote by the trend-cycle component of {yt} at time t At time t we observe yt and can update our estimate of and predict yt+1: = yt + (1 - )-1 yt+1|t = assuming we know - 1 We can estimate e0 using an MAS of e.g. the first 10% of the data and then obtain subsequent recursively from the Smoothing equation α is a smoothing constant such that 0 < α < 1 Smoothing Parameter in SES For a data set the optimal level of α can be determined by minimizing the sum of squared "in-sample" errors of one-period-ahead forecasts The minimization is performed numerically SES Forecasts SES has a "áat" forecast function SES forecasts have limited use beyond very short time horizon forecasts (similarly for MA smoothing) Nonetheless, SES weight distribution provides the backbone of many dynamic forecasting models that we will cover
