EN.553.413/613 Applied Stats and Data Analysis PRACTICE FINAL EXAM Question 1. For the following scenarios, indicate the most appropriate models that could be applied from the list below. Models: (A) simple logistic regression; (B) multiple logistic regression; (C) polynomial regression with one predictor; (D) polynomial regression with two predictors; (E) single factor ANOVA; (F) two-factor ANOVA; (G) polytomous logistic regression; (H) none of the above Scenarios: In all scenarios Y is the response variable; X, Xi are predictor variables. (i) Y is the time it takes to service a car (in hours), X1 is the brand of the car, X2 is the age of the car (in months); (ii) Y is whether a participant took a COVID shot or not, and X is the age of the participant (in years); (iii) Y is the weather tomorrow (sunny, cloudy or raining), X1 is the temperature today (measured in F), X2 is the pressure today (measured in Pa), X3 is the weather today (sunny, cloudy or raining); (iv) Y is the probability that a customer will buy a car, X1 is the brand of the car, X2 is the price of the car (in thousands of dollars); (v) Y is the flight delay (in minutes) of a major US airline in 2019, and X is the day of the week (from Sunday to Monday). Question 2. A company that is involved in importing rice is currently interested in the relationship between package weights and sales, e.g., are heavier packages easier to sell? The company designs a randomized experimental study in which the predictor variable X is the weight of the package (in kilograms) and the response variable Y is the number of packages sold in one month. The number of data points is n = 20. The company then runs a simple linear regression according to the model Yi = β0 + β1Xi + εi where the error terms εi are assumed to be independent normal random variables with mean 0 and constant variance σ2 (σ2 is assumed unknown). The following quantities were calculated from the data The following estimates were computed from the data b0 = −1.0361; b1 = 2.0284; MSE = 0.0315 From the above output, answer the following question. (a) (5pts): Compute the estimated variance for b1? (b) (10pts): Setup and conduct two tests (a t-test and an F-test) for the hypothesis that package weights has an effect on number of packages sold. Use a significance level of α = 0.01. State your conclusions. (c) (5pts): Can a t-test and an F-test be used interchangeably in the simple linear regression setting? Explain in your own words why yes or why no. (d) (5pts): What is the 95% prediction interval for Yh when the associated value of the predictor variable is Xh = 3 ? Hint: the following identity may be useful: Question 3. Answer the following collection of (unrelated) questions regarding matrices. (a) Let X be the following matrix Let y and z be the vectors Compute the projection of y onto Col(X) (column space of matrix X). Also compute the projection of z onto the Col(X). Explain why your answers make sense from the geometric point of view. (b) Let x = [X1, X2, X3] ⊤ be a random vector in R 3 with variance-covariance matrix Let a random vector Y = (Y1, Y2) ⊤ be defined by Y1 = X1 + X2, Y2 = X1 + X2 + X3. Compute the variance-covariance matrix Var[Y ]. (c) Let Y be a vector in R n, X is a n × q1 matrix and Z is a n × q2 matrix. Suppose that scientist A fits a linear model (using least squares) of the form. Y = Xβ + ε to y and scientist B fits a linear model (also using least squares) of the form. Y = Xβ + Zγ + ε. Scientist A obtains the estimate bA and scientist B obtains the estimates bB and cB (these are estimates of β and γ of the model B, respectively). It turns out bB = bA. What can you reasonably assume about the relationship between X and Z? Justify your answer. Question 4. Let Y = Xβ + ε be a linear model where ε ∼ Nn(0, σ2 In). Answer the following questions. (a) Suppose that X has full-column rank and that β is indexed as β = (β0, β1, . . . , βp) ⊤. Which of the following hypotheses tests can be formulated as a general linear hypothesis test H0 : Cβ − γ = 0? If the test can be formulated as a general linear hypothesis test, write down a test statistic and specify numerical values where possible. (b) Suppose Y = (Y1, Y2, . . . , Yn) ⊤. Let b be the ordinary least square estimates of β for the model Y = Xβ + ε and e = Y − Xb. Are the three quantities pairwise independent? Justify your answer. Above, 1 is an n-by-1 vector of 1’s. Question 5. Let U1, U2, . . . , Un1 be indepedent and identically distributed random variables with Ui being normally distributed with mean µ1 and variance σ 2 . Similarly, let V1, V2, . . . , Vn2 be i.i.d. normal random variables with mean µ2 and variance σ 2 , and W1, W2, . . . , Wn3 be i.i.d. normal random variables with mean µ3 and variance σ 2 . Also assume that the Ui ’s, Vj ’s and Wk’s are all independent. (a) Let y = [U1, U2, . . . , Un1 , V1, V2, . . . , Vn2 , W1, W2, . . . , Wn3 ] ⊤. Write y as a linear model y = Xβ + ε where β = [µ1, µ2, µ3] ⊤. (b) Find the least square estimate βˆ = [ˆµ1, µˆ2, µˆ3] ⊤ of β. (c) Write down (and simplify as much as you can), the expression for the sum of square error for the model in part (a). (d) Write down, and simplify as much as you can, the expression for the test statistic for testing the hypoth-esis H0 : µ1 = µ2 = µ3 against the alternative hypothesis that HA : µ1 ≠ µ2, or µ2 ≠ µ3, or µ1 ≠ µ3. What is the distribution of the resulting test statistic assuming H0 is true ? Hint: If µ1 = µ2 = µ3, then the model in part (a) reduces to y = 1µ + ε where 1 ∈ R n1+n2+n3 is a vector of 1’s and µ ∈ R. Question 6. The prostate dataset comes from a study on 97 men with prostate cancer who were due to receive a surgery. The variables are as follows ❼ lcavol: log(cancer volume) ❼ lweight: log(prostate weight) ❼ age: age ❼ lbph: log(benign prostatic hyperplasia amount) ❼ svi: seminal vesicle invasion ❼ lcp: log(capsular penetration) ❼ gleason: Gleason score ❼ pgg45: percentage Gleason scores 4 or 5 ❼ lpsa: log(prostate specific antigen) Consider the following output. data(prostate) x
APH402 Homework 1 Due on Tuesday, November 19, 2024 Question 1. To each of six 50.00 mL volumetric flasks, 5.00 mL of unknown solution containing X was added carefully. Then 0.00, 2.00, 4.00, 6.00, 8.00 and 10.00 mL (Vst) standard solutions with the concentration of X as 1.00 μg/mL were added to the flasks separately. The solution mixtures were diluted to 50.00 mL and were then analyzed using UV-Vis spectrometric method under 820 nm, giving absorbance (A) of 0.021, 0.045, 0.066, 0.084, 0.106, and 0.130 when wave path length was 1.00 cm. 1) Which calibration method was used in the experiment? Comment about its advantages. [6 marks] 2) What are the other TWO most commonly used instrumental calibration methods? Describe the process and the working principles for the two methods and their advantages. [12 marks] 3) Assuming that the absorbance of the solutions obeys Beer’s Law (A = εbc), derivate A~Vst equation and process the experimental data with Excel, showing the absorbance A ~ Vst diagram along with the R2 and the A ~ Vst equation, and calculate the concentration of X in the sample solution. [10 marks] 4) A spectrophotometric method for the determination of Pb2+ in blood uses Cu2+ as internal standard. A standard sample containing 1.75 ppb Pb2+ and 2.25 ppb of Cu2+ gives an absorbance of 0.48 and 0.20 for Pb2+ and Cu2+, respectively. To analyze an unknown sample, 10.0 mL of 2.50 ppb Cu2+ solution was added to 10.0 mL of the unknown blood sample, and then diluted to 25.0 mL with a volumetric flask. The absorbances measured 0.15 and 0.21 for Pb2+ and Cu2+, respectively. Calculate the concentration of Pb2+ in the unknown blood sample. [8 marks] Question 2. The van Deemter Equation relates the linear velocity (μ) with the height of the theoretical plate (H): H = A + B/μ + Cμ Explain the type of band broadening described by A and B terms with the help of diagrams. [8 marks] Question 3. GC separation of a 2-component mixture, with a 20-meter column, resulted in a chromatogram shown below. 1) Calculate the standard deviation (σ), the separation factor (α),the resolution (Rs), the plate number (N) and the plate height (H) of these two components. [12 marks] 2) How long should the column be to achieve a baseline resolution of these two peaks? And what are the retention times for components A and B in this new column? Question 4. Answer All Questions 1) Sketch diagrams to illustrate the effect of flow rate of the mobile phase (μ, cm/s) to the efficient of the column (H, mm) for GC and LC, respectively. [6 marks] 2) An average HPLC column available today is able to achieve column efficiencies (N) of around 40,000 theoretical plates/m. In comparison, even the best capillary GC columns are only able to achieve 8,000 theoretical plates/m. Why then, is GC capable of achieving orders of magnitude better resolving power than HPLC? Explain this apparent contradiction. [6 marks] Question 5. Reversed phase HPLC analysis was carried out for a pharmaceutical mixture contains 4-aminobenzoic acid (A), salicylic acid (S) and benzoic acid (B), where the stationary phase is a C18 chain with polyfluorinated aromatic ring at the end of the chain. If a 30% MeCN aqueous solution is used as the mobile with a UV 255 nm detector, discuss the necessity of controlling the pH of the mobile and the relative order of the retention times for the three analytes at the pH you proposed. [10 marks] Question 6. For the six solutes below, two different GC spectra were obtained with two different the stationary phases. Give your explanation based on the “Like interacts with like” theory. [20 marks] Exp#1: SP = 100% Methyl Polysiloxane Exp#2: SP = 50% Methyl + 50% Phenyl Polysiloxane
FBE 506 Quantitative Methods in Finance Assignment #5 1- Using monthly data for the period January 2014 to present compare the following finance ratios for JPM and Face Book: a. CV (coefficient of variation). b. Sharpe ratio c. Sortino ratio d. Traynor ratio What do those ratios imply on investment on the two securities. 2- JP Morgan or Face book: Download monthly data for JPM and Face book (FB) from January 2, 2018 to today, using adjusted closing prices. Which one is a better investment? 3. A portfolio’s return is normally distributed with mean 5% and standard deviation of 2.2%, both expressed in annual terms. Calculate 5% VaR as a percentage of the mean return when the risk horizon is one year, six months, one month, and one day. 4- Using the monthly data for AAPL, FB, and JPM for the period January 2018 to present calculate the parameters of the Markowitz portfolio model. 5. Variance Covariance Matrix and Asset Allocation Model (Markowitz Portfolio Model): Suppose the variance covariance matrix for two stocks is given as: Stock 1 Stock 2 Stock 1 0.025 0.015 Stock 2 0.030 The expected rate of returns on Stocks 1 and 2 are 10% and 12%, respectively. The average return to risk-free treasury is 5%. Given that the objective of the investor is a minimum-risk portfolio, find the optimum weights of each stock in the portfolio.
ECE5550: Applied Kalman Filtering THE LINEAR KALMAN FILTER 4.1: Introduction ■ The principal goal of this course is to learn how to estimate the present hidden state (vector) value of a dynamic system, using noisy measurements that are somehow related to that state (vector). ■ We assume a general, possibly nonlinear, model xk = fk−1(xk−1, uk−1,wk−1) z k = hk (xk, uk ,vk ), where uk is a known (deterministic/measured) input signal, wk is a process-noise random input, and vk is a sensor-noise random input. SEQUENTIAL PROBABILISTIC INFERENCE: Estimate the present state xk of a dynamic system using all measurements Zk = {z0 , z 1 , ··· , z k } . ■ This notes chapter provides a unified theoretic framework to develop a family of estimators for this task: particle filters, Kalman filters, extended Kalman filters, sigma-point (unscented) Kalman filters. . . A smattering of estimation theory ■ There are various approaches to “optimal estimation” of some unknown quantity x . ■ One says that we would like to minimize the expected magnitude (length) of the error vector between x and the estimatex(ˆ) . ■ This turns out to be the median of the a posteriori pdf f (x | Z). ■ A similar result, but easier to derive analytically minimizes the expected length squared of that error vector. ■ This is the minimum mean square error (MMSE) estimator ■ We solve forx(ˆ) by differentiating the cost function and setting the result to zero ■ Another approach to estimation is to optimize a likelihood function ■ Yet a fourth is the maximum a posteriori estimate ■ In general,x(^)MME /=x(^)MMSE /=x(^)ML /=x(^)MAP , so which is “best”? ■ Answer: It probably depends on the application. ■ The text gives some metrics for comparison: bias, MSE, etc. ■ Here, we usex(^)MMSE = E[x | Z] because it “makes sense” and works well in a lot of applications and is mathematically tractable. Some examples ■ In example 1, mean, median, and mode are identical. Any of these statistics would make a good estimator of x . ■ In example 2, mean, median, and mode are all different. Which to choose is not necessarily obvious. ■ In example 3, the distribution is multi-modal. None of the estimates is likely to be satisfactory! 4.2: Developing the framework ■ The Kalman filter applies the MMSE estimation criteria to a dynamic system. That is, our state estimate is the conditional mean where Rxk is the set comprising the range of possible xk . ■ To make progress toward implementing this estimator, we must break f (xk | Zk ) into simpler pieces. ■ We first use Bayes’ rule to write: ■ We then break up Zk into smaller constituent parts within the joint probabilities as Zk−1 and z k ■ Thirdly, we use the joint probability rule f (a , b) = f (a | b)f (b) on the numerator and denominator terms ■ Next, we apply Bayes’ rule once again in the terms within the [ ] ■ We now cancel some terms from numerator and denominator ■ Finally, recognize that zk is conditionally independent of Zk−1 given xk ■ So, overall, we have shown that KEY POINT #1: This shows that we can compute the desired density recursively with two steps per iteration: ■ The first step computes probability densities for predicting xk given all past observations ■ The second step updates the prediction via ■ Therefore, the general sequential inference solution breaks naturally into a prediction/update scenario. ■ To proceed further using this approach, the relevant probability densities may be computed as KEY POINT #2: Closed-form. solutions to solving the multi-dimensional integrals is intractable for most real-world systems. ■ For applications that justify the computational expense, the integrals may be approximated using Monte Carlo methods (particle filters). ■ But, besides applications using particle filters, this approach appears to be a dead end. KEY POINT #3: A simplified solution may be obtained if we are willing to make the assumption that all probability densities are Gaussian. ■ This is the basis of the original Kalman filter, the extended Kalman filter, and the sigma-point (unscented) Kalman filters to be discussed.
CIV6782 Climate resilient water infrastructure design – Coursework brief Part 1 – Document analysis (35 marks) Please pick one of the following three research papers: A. Quinn et al. (2018). Exploring how changing monsoonal dynamics and human pressures challenge multireservoir management for flood protection, hydropower production, and agricultural water supply.Link B. Bertoni et al. (2019). Discovering dependencies, trade-offs, and robustness in joint dam design and operation: an ex-post assessment of the Kariba Dam.Link C. Murgatroyd and Hall (2021): Selecting Indicators and Optimizing Decision Rules for Long- Term Water Resources Planning.Link State which documents you chose and answer the following questions. Please note the hard limit of two sides of A4 for answering all questions (either manuscript. or typed in Arial 11). Q1. State which documents you chose for the document analysis, and present the characteristics of the case-study it contains: a. Which geographic area does it deal with? b. What is the key water infrastructure considered here? c. What are key water uses and / or water-related risks considered here? d. Is it a planning problem, an operational problem, or both? e. What is according to you the main engineering issue tackled in this work? Q2. How is the problem framed: what is the XLRM? Q3. Is robustness well-defined? Please justify. If yes, how are metrics M used to define it? If not, please propose a way it could have been defined and justify why it would be appropriate. Q4. What are the key trade-offs discovered in the paper? Please state two ways in which the existence of trade-offs makes the problem “wicked” in this case, and why. Q5. This question is about the uncertainties considered in this document. a. How are “well-characterised” uncertainties modelled? Give an example. b. How are sources of “deep” uncertainty modelled? Give an example. c. Please name a source of uncertainty that could or should have been included, but is not. Q6. This question is about the stakeholders identified in the document. a. Can you name them, identifying which ones have competing interests and which ones have synergistic interests? b. Are the interests of all stakeholders identified in the papers evaluated through a metric? Please justify your answer. c. Is there a stakeholder that you can think of that is not represented in this study? [Indicative marking scheme: Q1 7 marks, Q2 8 marks, Q3 6 marks, Q4 5 marks, Q5 5 marks, Q6 5 marks] Part 2 – Water resource system analysis (65 marks) For this part, you will submit a Jupyter Notebook with your commented code, including answers to questions. There is no length limit but concision will always be regarded favourably, and lengthy text is worth no extra points. You can (and probably should) reuse and adapt code from the tutorials when relevant. Throughout your submission, please document any use of Large Language Models (LLMs; or alternatively, attach a statement on that use, < 300 words). Remember that use of Generative AI is allowed as long as it follows the University’sguidance. Each of you will be working a different set of input data. The column / line numbers associated with your coursework will be communicated to you by email within the next week under the subject line “CIV6782 Coursework data” . Task A: Operating current infrastructure (25 marks) Here is a schematic of a reservoir system serving water for irrigation and producing hydropower. There are also minimum ecological flow constraints downstream (Figure 1). The different uses are: I. Minimum ecological flows Qeco to be met all of the time. II. Meeting the irrigation demand Dirr (variable monthly, see data) served through a shallow intake. III. Meeting domestic demand Ddom (also variable monthly), served through a deeper intake. IV. Producing firm power Pf, i.e., a level of hydropower production to be met at least 99% of the time. This level is specified in your data sheet. Note that in this case it will be enough to meet a release target (i.e. enough to produce Pf if the reservoir is full). Figure 1. a) System schematic; b) Reservoir section. Q1. Assume a standard operating policy (SOP). Use tutorials and associated Python code, and adapt them to produce a water balance of the reservoir for the 70-year daily inflow time series you have been given. Your model should also output the following: ● For the four uses above, reliability, resilience and vulnerability. ● For uses I to III, volumetric reliability. ● Average annual hydropower production, as well as a time series plot of annual hydropower production. Q2. Still using code from the tutorials, propose alternative management policies that lead to a higher hydropower production. In particular: a) Is there a way to increase power production (compared with SOP hydropower production), without affecting other water uses? b) What uses appear to be in conflict? In synergy? Q3. In a few sentences, can you describe what you perceive to be the main qualitative differences between the Conowingo reservoir case used in the tutorial and this case? Task B: Planning problem and robustness (40 marks) Let us now assume we are looking into expanding irrigated area from the current surface. We assume we scale up the current mix of crops: this means the relative month-to-month consumption will not change (or said otherwise, if we expand irrigation by 20%, demand during all months will increase by 20%). We are considering irrigation expansion up to a doubling in withdrawn volumes. We also consider the possibility to reduce firm power requirements by up to 50% to protect irrigation water supply despite irrigation expansion. We assume the following objectives: 1. Irrigation volumetric reliability, to maximise. 2. Post-development irrigated surface area, to maximise. 3. Firm power requirement, to maximise. 4. Reliability of firm power production, to maximise. Q1. Please carry out a multiobjective analysis of irrigation expansion. Choose an appropriate visualisation strategy. Comment the results, in particular (but not limited to): a) What objectives are in conflict? b) What uses are impacted by the irrigation expansion, and which aren’t? c) How does the choice of policy from Task A (SOP or more hydropower-friendly) impact irrigation expansion? d) How would you select three solutions (irrigation expansion + operating policy) to carry out a vulnerability analysis? Q2. Let us now do a vulnerability analysis of the three solutions you chose. We assume flows could decrease by up to 50% on average, with low flows particularly affected, and flow variability will increase. We also assume climate change can increase crop irrigation demand (by up to 20%) and that domestic water demand may increase by up to 50%. a) Use the method from Tutorial 3 to generate a range of plausible flows for your analysis. b) Please conduct the vulnerability analysis to see in which climates the volumetric reliability of the most vulnerable use, as defined in Task A, falls below 85%. Please comment on the suitability of different designs. c) According to you, what potential impacts of climate change, both on streamflows and on irrigation demands, are being left out in this robustness assessment? Q3. Now imagine a 50% expansion of irrigated surface area is agreed upon, and decreasing firm power requirements are envisaged as an adaptation measure as / if supply-demand conditions deteriorate. a) Please describe your approach to implementing this adaptation measure, as well as the trade-offs this involves. b) Propose an adaptation plan following this approach.
IB3L40 Lean Startup Individual Assignment: Feasibility Report, 2024-25 Assignment Instructions All assignments must be submitted ONLINE via my.wbs by 12pm (midday) UK time on the date displayed against this assessment. Word Limit 3000 word limit. Word Count Policy WBS has a school-wide policy on word counts. This is strictly enforced to ensure consistency across modules and programme. You can find more information about this policy in the Undergraduate Student Handbook under Academic Practice -7i. Word count policy. This is a strict limit not a guideline: any piece submitted with more words than the limit will result in the excess not being marked. Academic Practice Please ensure you read the full guidelines forAcademic Practicein the Undergraduate Student Handbook and ensure you understand it. If in doubt, please seek clarification in advance of your submission. This includes important information on: • Cheating, plagiarism and collusion • Correct referencing • Using internet sources in assessments • Academic writing • English Language support • Word count policy When you submit this assignment online, you will be required to tick a declaration box indicating that the work involved is entirely your own. Each assignment will be put through plagiarism software to identify any collusion or inadequate referencing of materials used from different sources. Please do not submit images of your typed work unless you have been specifically requested to do so. We would consider taking action if your work: 1. is too reliant on the words of particular authors (rather than presenting your ideas in your own words), if the essay uses the ideas or words of an author without referencing them or putting their words into quotations (plagiarism). 2. suggests that you have worked very closely with another student or students (unless explicitly asked to do so by your Module Leader/Tutor) (collusion). 3. includes unreferenced work that you have previously submitted for any accredited course of study (unless explicitly asked to do so by your Module Leader/Tutor) (self-plagiarism). The Use of Artificial Intelligence (AI) The University recognises an increasing number of technologies such as Artificial Intelligence and that they maybe applicable in your completing this assessment. The assessment brief sets out specific requirements or restrictions, and theUndergraduate Student Handbookhas further guidance and advice. You are reminded that the inappropriate use of such a technology may constitute a breach of University policy, such as theProofreading PolicyorRegulation 11 (Academic Integrity). If you breach these policies, it may have significant consequences for your studies. Please make sure you read and understand the assessment brief and how AI mayor may not be used. If a generative AI or similar is permitted and has been used you MUST make clear why you used such a tool or service, what you used it for and you will be obliged to confirm that you take sole intellectual ownership of any submitted work. As appendices, and as part of your submitted work, you must provide screenshots of the question and the AI-generated response, alongside an explanation of how the content has been utilised. You should note the relevant reference alongside each screenshot. When you submit you must complete (physically or electronically) a declaration. This requires you to explain the use of any AI. Failure to disclose at the point of submission maybe prejudicial in any later investigations should they arise. For this assessment the use of AI is: • Prohibited Where AI is prohibited: You MUST NOT use any generative Artificial Intelligence in this assessment unless specifically authorised for reasonable adjustments. You MAY use non-generative tools such as a spell-check, basic grammar check (non-generative), calculator or similar. If you have any doubts about a tool or service you plan to use please contact the module leader. Extensions and Self-certification Late submissions will incura penalty of 5% for every 24 hour period after the due date and time,i.e. this begins one minute after the submission deadline (beginning at 12.01pm). Requests for specific extensions (of up to 15 days) which are typically for longer and more serious concerns must be submitted via my.wbs ideally 72 hours BEFORE the deadline. Extensions can only be approved if you clearly detail your circumstances and provide supporting documentation (or a reason as to why you cannot provide the supporting documentation at the time) asset out in the Mitigating Circumstances Policy. Self-certification is a university-wide policy whereby you are permitted an automatic extension of 5 working days on eligible written assessed work without the need for evidence. WBS permits self-certification for all types of written, assessed works such as essays and dissertations. It is not permitted for exams, course tests, or presentations. You can self-certify twice within each year of study, starting from the anniversary of your course start date. This will coverall eligible written assessments that fall within the self-certification period, as long as they have not previously had an extension applied. To find out further details about the self-certification policy please see:https://my.wbs.ac.uk/-/academic/20778/item/id/1244460/ . If you wish to self-certify for an extension of 5 working days, please select 'Self-certification' in the Extension Type field. If you wish to request a longer extension than 5 working days, please leave the Extension Type as 'Standard'. Your assignment instructions begin below. Assessment Task - 90% individual assessment The aim of this assessment is for you to test the feasibility of a new business idea that you will develop through this module. This assessment is about the process you go through, regardless of the end result i.e., whether or not your business idea is feasible. For this assessment, you should aim to show a clear and logical pathway that you took to determine feasibility. You should writeup and explain the process undertaken using the following structure as a guideline: 1. Overview of your business idea (refer to Business Model Canvas/Lean Canvas) and goal (suggested 300 words). 2. Assumptions - describe how you arrived at the 3 riskiest assumptions in the Assumptions Matrix for your business idea and clearly state your 3 hypotheses. Include the Assumptions Matrix and Table of ‘Assumptions to Hypotheses’ (suggested 500 words). 3. Tests - Note: you should use two different primary research methods and one secondary research method. Explain why each method was chosen to test the assumption. Describe who/what it was conducted with and how it was conducted (suggested 750 words per method, 250 words in each of 3 sections). 4. Result and analysis of each test conducted i.e., was each hypothesis proven true/false? And what do the results mean overall for your business idea – should you pivot or persevere? Is it feasible or unfeasible? How did the business idea (refer to Business Model Canvas/Lean Canvas) and goal change? What are the nextsteps? (N.B. Not sure/inconclusive may also be outcomes) (suggested 500-600 words). 5. Learning - what have you learned from the process about the strengths and weaknesses of the lean startup method (suggested 200 words)? • References - it is recommended that you cite journal articles or secondary data such as credible industry or market research to help build your arguments where possible (around 8-10 citations are suggested as a guideline and should be included as relevant/appropriate). • Appendix - any items in the appendix should have a brief explanation about what the item is and why it is included. For instance, 1-2 sentences starting with “This shows …” Appendices should not exceed 10 pages. As part of the appendix, please include the following: o A copy of the business model canvas/lean canvas you started with o A copy of the revised business model canvas/lean canvas that marks clearly what you tested and what changed after you tested the assumptions. Marking criteria This assessment will be marked using the WBS UG Generic 20-point Marking Criteria. All criteria will be equally weighted. The four pillars will be applied to this assessment: 1. Comprehension – knowledge and understanding of the subject matter 2. Analysis – logical arguments supported by evidence 3. Critical Evaluation – questioning relevant arguments by identifying their strengths and weaknesses 4. Academic writing – presenting a clear and structured assignment; use of relevant literature; academic honesty, referencing and citation Assessment criteria While each report section is not limited to one particular assessment criterion, below outlines the areas where you should aim to clearly demonstrate each one and from where the marks will largely be drawn from: • Providing a clear overview of your business idea and articulating a clear goal (largely comprehension) • Providing a clear account of how you arrived at the 3 riskiest assumptions for your business idea and a clear statement of your 3 hypotheses (comprehension and analysis) • Clearly explaining the methods chosen and how the tests were conducted (comprehension and critical evaluation) • Demonstrate logical arguments linked to data i.e., was each hypothesis true/false? And what do the results mean overall for your business idea - pivot or persevere? Feasible or unfeasible? (largely analysis) • Evaluation of the lean startup method strengths and weaknesses (largely critical evaluation) • Academic writing - please note academic writing will be assessed for the report as a whole. All criteria are equally weighted.
Assignment 3: Individual Report Assignment 3 is assessed on an INDIVIDUAL basis. Your solutions should be uploaded to Moodle as a single written document, which may contain graphics and mathematics (but which does not just list your code), and which makes clear links to well-labelled MATLAB files which should be uploaded to Moodle (as .m files) at the same time. Your solutions may be in PDF (e.g. generated via LaTeX), Word, or any other appropriate format, but they must NOT depend on the marker having access to any additional software beyond a PDF reader, Microsoft Word, and a copy of MATLAB. Credit will be given for providing working code, and for providing suitable comments within the code to allow it to be used accurately. Simply submitting a collection of MATLAB files is not enough. There are 5 questions in this assignment, with the marks for each question indicated at the end of the question. Your report needs to be submitted to the VLE submission point by 4pm on Friday 10th January. Late submission. Late submissions without penalty are only allowed for participants who have been granted an extension. To request an extension please see the relevant form. on the moodle. Otherwise, the project is subject to the standard University policy: “Work which is up to one hour late will have five percent of marks deducted. After one hour, ten percent of the available marks will be deducted for each day (or part of each day) that the work is late, up to a total of five days, including weekends andbank holidays e.g. if work is awarded a mark of 30 out of 50, and the work is up to one day late, the final mark is 25. After five days, the work is marked at zero.” For more details, see Guide to Assessment, Standards, Marking and Feedback. Academic misconduct. Any collaboration with your fellow students should be avoided. The work submitted for assessment must be yours and yours alone. Remember that there are severe penalties for academic misconduct offences such as plagiarism and collusion. For more details, see Guide to Assessment, Standards, Marking and Feedback . Supply and Demand Model The assessment is based around a supply and demand model introduced in the paper “ Landscape and flux theory of non-equilibrium open economy” by Zhang & Wang (link - also available at the VLE submission point). The model is described in section 2.2 of the paper and models the balance of price P and quantity Q in a single market. It should be noted that these values are the deviation from equilibrium values, so a negative price refers to a price below the equilibrium value. The relevant equations are equation 6 in the paper; As in the paper, we are assuming that the speeds of adjustment (labelled α, β in the paper) are both equal to 1. The remaining parameters ab, c and d are related to the demand function and supply curve as defined in the paper. You can refer to the paper in your report, but if you wish to use any of the figures to aid in your answers to the questions then you should produce your version of the figure. For example, it may be useful to include your own version of figure 1 from the paper. Question 1 First, we consider the case where supply is linear (i.e. b = 0). Find & classify all fixed points using the parameter values a = − 1, b = 0, c = 1 & d = − 1. Sketch a phase portrait, indicating any interesting features. Discuss your results in the context of the model. [20 marks] Question 2 Considering the full model with (b ≠ 0), produce phase plots of the system for parameter combinations i) a = − 0. 5, b = 0. 3, c = 0, d = − 1. ii) a = − 0. 1, b = 0. 3, C = 0, d = − 1. iii) a = 0. 5, b = 0. 3, C = 0, d = − 1. Annotate any relevant features and discuss your results and the main features of the system’s behaviour in the context of the model. Repeat your analysis of parameter set i) and iii) with C = − 0. 3, again discussing any interesting behaviour. [25 marks] Question 3 Using an appropriate modification, add the effect of seasonality to the model. Use the parameters from Question 2. iii). Justify and discuss your choice of seasonality, including your choice of parameter values and/or ranges, and explain the consequences of these changes using example trajectories and/or phase plots. [20 marks] Question 4 Use an appropriate modification to include noise into the system so that it can be represented as a system of stochastic differential equations (SDEs). Provide a careful justification for your choice of noise term(s). Use the parameters from Question 2. i) Discuss the results using example trajectories, including interesting behaviours induced by the noise. [20 marks] Question 5 Summarise how your findings could be used by governmental regulators, or by businesses, to guide their actions and ensure stable market behaviours. Your answer should be a maximum of 250 words and should be accessible to a non-specialist audience. [15 marks]
SEES008 Example exercises Quantitative Methods 24/25 This document contains more question sets than the final exam. Questions in this document can be used as reference for the form and the difficulty level. It aims to have a full coverage to all topics taught in this module. Question set 1: Probability table A market research group specialize in providing assessments of the prospects of shopping centre. In a city famous for tourism, the group categorize that 60% of the tourism shops are successful, and 40% are unsuccessful. The group assess products as good, fair and poor. In those successful shops, the products are assessed as good for 70%, fair for 20% and poor for 10%. For those shops turned out to be unsuccessful, the products are good for 20%, fair for 30%, poor for 50%. Let event G = the quality of product as good Let event S = the prospect of a store is successful Answer the following questions: 1.1. Draw a probability table which include events G and event S with corresponding probabilities 1.2. For a randomly chosen store, what is the probability that products will be assessed as good? 1.3. If products for a store are assessed as good, what is the probability that it will be successful? 1.4. Are the events G and event S statistically independent? 1.5. Suppose that 5 store are chosen as random. What is the probability that at least one of them will be successful? Question set 2: CI and Chebychev’s theorem Suppose we have a random sample of size n = 100 from a population. We find that the sample mean =80, sample standard deviation s=30. The probability that the population mean μ falls into an interval between two points a and b is 90%. Point a and b is symmetric to the central point of distribution. 2.1 What is the terminology for the range between point a and b? 2.2 What is the terminology for the value of point a and point b? 2.3 If the population is normally distributed, select an appropriate value from table below and calculate values of a and b. Justify your choice from the table. Explain your calculation in all necessary intermediate steps. 2.4 (4 points) If the shape of the population distribution is unknown, How to find out the values of a and b? Explain your answer with expressions to define a and b. Question set 3: Fuel economy problem You are in a project studying the impact of technology innovation on fuel economy. Specifically, you are studying the performance of vehicle, which is measured by variable milpgal as the miles per gallon the vehicle can travel. A data set is obtained from collecting information through 4 companies. Variable company contains numerical values 1, 2, 3, 4 for data collected from company 1, company 2, company 3 and company 4. Company 1 and 4 are leading companies in the industry and they have adopted an innovative technology before the period of data collection. Please answer the following questions: 3.1 Explain the meaning of the following command and describe what change can be expected to the data set after executing the command: gen inno=(company==1 | company==4) 3.2 Next, a Box-Whisker graph has been created to study the distribution of subsets data. In the box-whisker plot below, the dash line denoted with mean_ 1 refers to the mean of first subset. the solid line denoted with mean_2 refers to the mean of the second subset. A histogram has also been created to show the distribution of the same subsets. Do you expected the two distribution of subset data to be normally distributed? Justify your answer by using evidence in the box-whisker plot and histogram. 3.3 You decided to perform Shapiro-Wilk test on both subsets and obtained the following Stata results by command bysort inno: swilk milpgal. Assuming 5% significance level, are milpgal distributions normally distributed? Justify your answer by using the Stata output. 3.4 We next calculated the confidence interval for both subsets at 95% confidence level. The Stata output is below. Is it possible that the population of two subsets milpgal have equivalent mean? Justify your answer. Question set 4: fuel economy comparison You are joining a project on factor study in fuel economy. The project is making good progress, where several effective factors have already been identified by using a cross-sectional data set. You are looking for other potential factors to explain the differences in miles per gallon a vehicle can travel. Variable milpgal measures the miles per gallon that a vehicle can travel. Variable price measures the selling price of vehicle. You’re investigating whether on average a more expensive car can travel more miles per gallon. The data set records information collected from 4 different companies. You start the investigation on the impact of variable price by using the following Stata commands. The output is also the following: 4.1 Explain what the Stata command shown above is trying to achieve gen Hprice=(price>r(mean)) and predict the range of values contained in variable Hprice. Then you perform. a hypothesis testing on the subsection data created from question 1. It is assumed that the variances of populations for both subsections equal. 4.2 What is the H0 and H1 in the test implemented by command ttest milpgale, by(Hprice) as shown above? Formulate H0 and H1 and interpret them. 4.3 3 cases of hypothesis testing results are produced, as shown in the last 2 lines. Set significance level to 5%. What conclusion do you arrive at with referring to each of three cases of the test? Combining results from 3 sets of tests altogether, what is your conclusion? 4.4 Based on your conclusion in question 3, will you advise to include variable Hprice into the model to help explain the change in variable milpgal? Justify your answer. Question set 5: Flight seat overselling problem Suppose that you’re in charge of marketing airline seats for a major carrier. You’re focusing on the flight ticket overselling problem. Four days before the flight date you have 16 seats remaining on the plane. You know from past experience that 80% of people that purchase tickets in this time period will actually show up for the flight. You are estimating the potential losses when selling 18 tickets. Set the random variable X as the number of occupied seats on the flight day. To help this analysis, a cumulative probability table produced by Stata function binomial(n,k,p) is provided as follows, with p=0.8 and different values of number of trials n and number of success k: 5.1 What distribution does the random variable X comply with? 5.2 What is the probability to have 1 empty seat? 2 empty seats? 3 empty seats and 4 empty seats on the flight day? Calculate the probabilities and demonstrate any necessary intermediate steps involved. 5.3 Graph the probability density function of the 4 scenarios based on your calculations in question (b). State the name of the variables which denote y and x axis in your graph. 5.4 You learned that the airline company on average suffers the following losses in values for this route in these 4 scenarios: What is the expected value of loss caused by having number of empty seats ranging from 1 to 4?
IBIS1100 ASSESSMENT 2 BUSINESS TECHNOLOGY CASE STUDY In-person presentation during scheduled tutorial sessions. All members should present their part. No online presentations unless you have a valid reason. In groups of 3-4, select an organization that has recently adopted an information system solution OR could benefit from an information system solution, to address a challenge in their business process or optimize their business operations. Your task is to investigate how the chosen organization has utilized specific Information Technology/Information Systems (IT/IS) solutions to enhance its business process efficiency and/or competitive advantage. Using the Creating Business Value with IT (CBVwIT) framework, develop a detailed case study addressing the following steps: 1. Identify the Specific Business Problem(s) o Identify the specific business problems your chosen organization need(ed) to address. o Explain the problems using competitive forces and value chain. 2. Determine Business Strategy & Processes o Determine the desired business strategy and the business processes needed to create business value. o Describe the strategic direction the organization decided upon to address the identified problems and the specific actions required to achieve it. 3. Recommend Specific Information Systems o Identify the Information Systems (IS) that will support the organization’s strategic plan. o Explain how these systems will help address the business problems and support the business strategy and processes. 4. Determine ICT Infrastructure Requirements o Establish the ICT (Information and Communication Technology) infrastructure necessary to implement the proposed information system. o Describe the support and resources needed to properly support the strategic plan. Presentation duration: 15 - 20 minutes followed by 5 – 10 minutes Q & Aduring your scheduled tutorial session. Submission: Submit your presentation slides the night before your presentation day. Note to Students: This is a presentation-only assessment, so it is crucial to present your content well since there are no other content submissions. You can include explanatory notes or detailed speaker notes in the speaker notes section of each slide to ensure all points are covered during the presentation and to enhance clarity and ensure comprehensive coverage of the assignment requirements.
COM153 Audiences and Media Users Semester 1, 2024-2025 Final project Date of submission: 12 at noon, Dec. 27th, 2024 Format: Written report Word limits: 1700 words +/- 10%, excluding bibliography Submission method: LMO Weight: 70% of grade Learning outcomes assessed: B, C, D Assignment Brief: The final project assessment of COM153 invites you to individually conduct a small-scale audience research project regarding a topic of your own choice. You can build your project upon the topic you chose for the first assessment group presentation and take into consideration your teachers’ feedback for your presentation. You can also choose a new topic if you want. Your final project must be related to at least one of the weekly topics covered by the module while at the same time utilizing one of the common research methods of media audience studies—namely, experiment, survey questionnaire, in-depth interviewing, media autobiography, and participant observation. You are required to finish a written report of 1700 words. The major aim of this assessment is to evaluate students’ reflection on the power relations between audience and media, as well as your ability to recognize, explain and apply relevant concepts and theories of media audience studies to analysing a recent audience phenomenon with the help of appropriate research method(s). What this assessment expects from you is a preliminary audience research project which can be further expanded at a later stage. This means choosing to do one (or at most two) of the following types of empirical research: ž Conducting two to three in-depth interviews ž Collecting 20 to 30 survey questionnaires ž Doing participant observation with one group of research subjects ž Recruiting five participants for media audio-biography ž Carrying out one pair of controlled experiments Your written report should be structured in the following manner: 1. Introduction: please first give some background information about your research topic and question. Here, you can also mention how this audience phenomenon has been discussed by media commentators and publics as well. Then provide an overview of your research: what kind of theories and concepts do you engage with? What research method did you utilize? (around 250 words) 2. A brief theoretical discussion: use this section to elaborate on the theories and concepts that you find useful to analyse the audience phenomenon of your choice. Try to relate your theoretical discussion with your research topic. A brief literature review is welcomed, but not necessary (around 250 words) 3. Methodology: what kind of research method did you choose, why, and how did you implement it? Report briefly your research design. For example, what are the main questions you ask in your survey and topic guide; what types of data you collect in your ethnography work, etc. (around 200 words) 4. Findings and discussions: report your findings based on the preliminary fieldwork. Try to connect your findings to the the relevant concepts and theories you mentioned in section 2. For instance, how your findings confirm, enrich or challenge them. (around 750 words) 5. Conclusion and reflection: in this final section, please first summarize the main points you would like to make for your research project. Then briefly reflect on how your project could be further expanded or improved in terms of its research design. (around 200 words) Appendix (optional): if you want, you can attach your survey questionnaire, interview topic guide, statistical analysis, or any other research tool as an appendix. This part is not included for word count and not marked. Including an appendix helps you to better present your research design and prove you have done the research. Other requirements: 1. Do proper referencing and follow the APA system. 2. You should at least refer to two module readings as academic sources. On that basis, you could consider referring to extra-curricular academic sources. The minimum number of references for this coursework is five in total. 3. Feel free to include visual elements (diagrams, tables, figures, etc.) in your report. Pay attention to citing them properly following the APA system as well. 4. Give your report a title. 5. Insert page numbers to facilitate the feedback process. 6. Save your written report in PDF file and name it as COM153 final project_ student number. 7. Attach coursework cover sheet as the first page. 8. Submit it to the dropbox “COM153 final project submission” before 12 at noon, Dec. 27th, 2024 What are you expected to demonstrate? 1. Choosing a suitable audience research topic (10%) 2. Ability to recognize and explain key theories, concepts and methods of media audience studies (30%) 3. Applying theories and concepts of media audience studies to analyse a recent audience phenomenon (30%) 4. A critical understanding of and reflection on the power relations between audiences and media industries, and/or among different social groups of media users (20%) 5. Clear language and good referencing (10%)
SEMESTER 1 2024/25 COURSEWORK BRIEF: Module Code: MANG6556 Assessment: Individual Coursework Weighting: 100 Module Title: Credit Risk & Data Analytics This assessment relates to the following module learning outcomes: A. Knowledge and Understanding A1. Understand the potential of CRISP-DM and data analytics, particularly in the retail lending sector. A2. Demonstrate a critical understanding of different types of data analytics methods and the problems they can solve. A3. Interpret the output of statistical techniques used for the main data analytics applications. B. Subject Specific Intellectual and Research Skills B1. Identify the statistical models appropriate for analysing the various decisions that confront a data analyst in different industries. B2. Work with software to develop data analytics solutions, such as predictive scorecards, clustering models, and different types of regressions. B3. Assess the relevance of statistical package outputs to the decisions being addressed. C. Transferable and Generic Skills C1. Critically analyse practical difficulties that arise when implementing retail credit risk models; understand the cross-fertilisation potential to other business contexts (e.g., fraud detection, marketing, CRM, etc.). C2. Demonstrate an ability to use world-class software and to interpret its output in the relevant techniques. C3. Manage time and tasks effectively in the context of individual study. 3 Coursework Brief: Question 1 (70 marks) The dataset ‘Credit data.xlsx’ contains data on 10,000 borrowers and whether they subsequently experienced serious delinquency (see variable ‘SeriousDlqin2yrs’). Assume the lender now wishes to use this data to build a credit scoring model that predicts serious delinquency based on the other variables. The dataset contains the following variables: Variable Name Description SeriousDlqin2yrs Person experienced 90 days past due delinquency or worse RevolvingUtilizationOfUnsecuredLines Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits age Age of borrower in years NumberOfTime30-59DaysPastDueNotWorse Number of times borrower has been 30-59 days past due but no worse in the last 2 years. DebtRatio Monthly debt payments, alimony,living costs divided by monthy gross income MonthlyIncome Monthly income NumberOfOpenCreditLinesAndLoans Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards) NumberOfTimes90DaysLate Number of times borrower has been 90 days or more past due. NumberRealEstateLoansOrLines Number of mortgage and real estate loans including home equity lines of credit NumberOfTime60-89DaysPastDueNotWorse Number of times borrower has been 60-89 days past due but no worse in the last 2 years. NumberOfDependents Number of dependents in family excluding themselves (spouse, children etc.) 1.1 Carefully pre-process the data set by considering the following activities (35 marks): • exploratory data analysis • missing value handling (if any) • outlier detection and treatment (if any) • categorisation of the continuous variables (if deemed useful) • Weights of Evidence coding (note that some additional coarse classification might be needed). • Splitting the data set into a training and test set. 1.2 Estimate a scorecard using a logistic regression classifier and report the following (35 marks): • The most important variables • The impact of the variables on the target • The performance of the model. Use various performance metrics and discuss their relationship if any. • Result of scorecard. • Compare this scorecard with the results of a Random Forest. Discuss your results. • Why do banks typically use Logistic Regression as their base classifier? What do banks win and lose by doing this? Please carefully report the various steps of your methodology and discuss your results in a rigorous way! NOTE: It is unlikely that different students will come up with the exact same parameter estimates. Special consideration will be given to submissions whose estimates are identical. Question 2 (30 marks) Find an academic paper published in 2021 or later (based on online or print publication date) discussing a real-life application of data analytics. It is important that the dataset analysed in the paper consists of real-life (not artificial) data. The publication outlets in which to look for a suitable paper are: • Management Science • Operations Research • INFORMS Journal on Computing • INFORMS Journal on Applied Analytics • Journal of Machine Learning Research • European Journal of Operational Research • Production and Operations Management • Manufacturing & Service Operations Management • ICDM (The IEEE International Conference on Data Mining) • NeurlPS (Conference on Neural Information Processing Systems) • KDD (ACM SIGKDD Conference on Knowledge Discovery and Data Mining) The other journals which are not on the list are not acceptable. 2.1 Once you have found an appropriate paper, report the following in separate subsections (15 marks): • Title, authors, and complete citation (e.g., journal name, volume/issue, year, …) • The data mining problem considered • The data mining techniques used • The results reported • A critical discussion of the model and results (assumptions made, shortcomings, limitations, …) 2.2 Apply the methodology you reviewed into the dataset of ‘Credit data.xlsx’ and report the analytic steps, model performance, and business implications. (15 marks) Make sure you demonstrate that you understand what the article is all about and are able to provide a critical discussion. Do not copy and paste from the article. Using Turnitin, this will be easily detected! NOTE: The reviewed methodology should be different from methods applied in Question 1.
BEAM078 | BEFM022 Assignment version: 2024_25 Weighting: 100% Word Limit: 4000 (excluding tables, references, and appendices) Deadline: 10th January 2025 12pm Assignment brief: The MSc degree is awarded for demonstration of advanced knowledge of the research methods within the accounting/finance discipline, advanced knowledge of a specific field of research, advanced knowledge of data-gathering methods, scholarship in use of sources relied on including analytical tools, and a contribution to knowledge development in accounting/finance. BEAM078 and BEFM022 provides students with the basic building blocks with which to complete these fundamental aspects of the degree program. Your BEAM078/BEFM022 assignment concerns corporate failure (bankruptcy) prediction and you are required to answer the research task below by way of empirical analysis. Task: The year 2023 saw a large number of companies filing for bankruptcy across the globe. Your assignment will focus on US corporations which filed for Chapter 11 bankruptcy during this year. Using the list of companies provided, you are tasked with developing a model which can predict the failure of these companies using data one to five years before bankruptcy occurs. Your model(s) must be tested for accuracy and adhere to the minimum requirements detailed within this document. You are to draw upon both the pre-existing academic literature and any other factors which you feel may play an important predictive role within the given timeframe. Requirements: Using the tools provided, you are required to conduct independent research to investigate and complete the research task. Your final written document should adhere to the following structure: • Abstract • Introduction • Literature Review • Methodology • Data • Results • Conclusion • References • Appendices (where appropriate) • Guidance: You will be provided with a list of 10 US corporations which failed (struck off the stock exchange) during 2023 after filing for Chapter 11 Bankruptcy. These companies will be the primary focus of your analyses. You are required, using the techniques learned within this module, to assess the ability of accounting ratios (and other factors if necessary) to predict bankruptcy. The failed company group must be compared a group of healthy non-bankrupt companies within the sametime period. A list of non-failed companies (also provided) consists of two healthy companies for every one bankrupt firm. These have been matched according to both industry and size (total assets). You may expand on the number of non-failed companies if you wish by investigating the “entire database” from WRDS, in a similar manner to how we approach the example analysis in the class workshops. The task must be answered by independently selecting 5 or more financial ratios (or other independent variables)– for which the rationale for their selection and inclusion must be clearly defined. Examples and data collection methods will be provided throughout the course. The assignment and the research task are deliberately and broadly positioned. Given the voluminous literature and the many different ways authors have attempted to tackle this question in the past, students are granted a free license to answer the questions in any means they feel appropriate, provided that the sample of US companies provided to them isused within the analysis. A minimum requirement to pass this module is that students should be able to determine if there any significant differences between failed and non-failed firms with regards to a series of single accounting ratios (at least 5 of your choosing). Using these single (univariate) measures, students should demonstrate the ability to incorporate univariate measures into a single (or series of) multivariate model(s). Univariate and multivariate models must be measured for accuracy (the ability to separate failed and non-failed companies), and in an appropriate manner (e.g., t-tests. contingency tables, ROC curves). No validation of model accuracy is required. Given the contemporaneous nature of this study there is limited data available for a validation sample. Students may wish to base their work upon a prior published study (e.g. Altman, 1968), this is acceptable provided that the replication contains a univariate and multivariate element and that a detailed discussion is provided as to why the use of the model is appropriate under the given setting. If this approach is taken then inferences must be drawn between the findings of the original paper, and those findings produced by the student. Differences are likely to reside in the accuracy and coefficients of the model are will likely be due to country of study, the era in which the study was conducted, and the types of company which were analysed. These must be detailed and explained in detail within the results. Credit will be awarded for originality and plagiarism in any form is not tolerated by the University of Exeter Business School. The assignment is an individual piece of research and must be treated as such. Students will be assessed on their ability to: • Demonstrate knowledge and understanding of the research topic; • Critically appraise relevant extant research; • Provide a clear understanding of the methodology; • Interpret results; • Produce a clearly written, well-structured assignment.
Math 425 Fall 2024 - HW 13 Due Friday 11/29, 11:59pm, via Gradescope Please note: (1). Please include detailed steps. Only providing the result will not get full credits. (2). Please write at most one problem in each page. If you reach the bottom please start a new page instead of writing two columns in one page. If a problem contains multiple small questions, you may write them in one page. (3). Please associate pages with problems in Gradescope. 1. Suppose X = Uniform(−1, 2). When X = x, suppose Y is uniformly dis-trubuted between x and 2x. Use conditioning to find E[Y]. Notice: Be careful with the integration bounds when x < 0. 2. Choose an integer randomly among {1, 2, 3, 4, 5}, denoted by random variable X. Then choose an integer randomly among {k : 1 ≤ k ≤ X}, denoted by Y . Use conditioning to find E[Y]. 3. A die is continually rolled until the total sum of all rolls exceeds 300. Ap-proximate the probability that at least 80 rolls are necessary. Hint: Let Xi be the result of the i-th rolling. We need to approximate P(X1 + · · · + X79 ≤ 300) using the central limit theorem. 4. Amy has 100 light bulbs whose lifetimes are independent and identical ex-ponential random variables with an expectation of 5 hours. If the bulbs are used one at a time, with a failed bulb being replaced immediately by a new one (ignore the time for replacing), approximate the probability that there is still a working bulb after 525 hours. Hint: Let Xi be the working time of the i-th light bulb. We need to approximate P(X1 + · · · + X100 ≥ 525). 5. Engineers believe that W, the amount of weight (in units of 1000 pounds) that a bridge can withstand without structural damage resulting, is normally distributed with mean 400 and variance 402. Suppose that the weight (in units of 1000 pounds) of a car is a random variable with mean 3 and variance 0.32. Approximately how many cars would have to be on the bridge for the probability of structural damage to exceed 0.1? 6. An insurance company has 10,000 automobile policyholders. The expected yearly claim per policyholder is 240 dollars, with a standard deviation of 800. Approximate the probability that the total yearly claim exceeds 2.7 million dollars. 7. We have 100 components that we will put in use in a sequential fashion. That is, component 1 is initially put in use, and upon failure, it is replaced by component 2, which is itself replaced upon failure by component 3, and so on. If the lifetime (in hours) of component i is exponential random variable with mean 10 + 10/i. Use Markov’s inequality to estimate the probability that the total life of all components will exceed 1200 hours. 8. If X has mean µ and standard deviation σ, the ratio r = σ/|µ| is called the measurement signal-to-noise ratio of X. The idea is that X can be expressed as X = µ + (X − µ), with µ representing the signal and X − µ the noise. If we define D = |µ/X−µ| as the relative deviation of X from its signal (or mean) µ, show that for α > 0 we have P{D ≤ α} ≥ 1 − r2α2/1
CHEM20711 CONTEMPORARY THEMES IN CHEMISTRY 1. Answer ALL parts. (a) Answer BOTH parts (i) and (ii). (i) Explain what is meant by the term sustainable development goal. Discuss the need to improve access to safe drinking water in many parts of the world. (6 marks) (ii) Describe a technology that is currently used to produce drinking water by desalination of seawater. (6 marks) (b) Answer BOTH parts (i) and (ii). Circular nanofiltration membranes of effective diameter 3.00 cm consisting of MoS2 nanosheets on aporous support were prepared. They were then functionalised with either sunset yellow (SY) dye or crystal violet (CV) dye. Measurements were made of the volume (V) of water permeating through the membranes in a given time (t) under external pressure. Ion permeation experiments were also carried out and the % Na+ rejection evaluated. The results of these measurements are given in the following table. 3t +rejectionPristine MoS 2+SY263097MoS (i) Calculate the water flux in units of L m−2 h− 1 for each membrane. (8 marks) (ii) Discuss the potential of these membranes for water treatment applications. (4 marks) (c) The 2008 Robeson upper bound for the CO2/ N2 gas pair is given by the equation: P = kαn where P is the CO2 permeability in barrer, α is the CO2/ N2 selectivity, and the constants k and n have the values 30,967,000 and −2.888, respectively. The following table gives experimental values of P and α for mixed matrix membranes of the polymer of intrinsic microporosity PIM-1 in combination with three different metal-organic frameworks (MOFs). Determine whether each membrane lies below or above the 2008 Robeson upper bound. Comment on your results. / α (6 marks) 2. Answer ALL parts. (a) Answer BOTH parts (i) and (ii). Two zeolites have the respective chemical formulae: Na{(C2H5)4N}x [Al5Si38O86]·20H2O and Rb2Caz [AlySi16O48]·14H2O (i) Determine, with explanation, the values of x, y and z. (6 marks) (ii) Describe the possible roles of the organic ion in Na{(C2H5)4N}x [Al5Si38O86]·20H2O during formation of the zeolite framework. (5 marks) (b) Answer BOTH parts (i) and (ii). (i) Describe, with examples, three different types of catalytically active species that can be introduced into zeolites and the method of formation of each type of catalytically active species. (7 marks) (ii) State, with explanation, and using all the information provided, the type of shape selective catalysis occurring for the following zeolite catalysed reaction. The product distribution of the reaction is not affected by varying the size of the zeolite particles. (4 marks) (c) Answer BOTH parts (i) and (ii). (i) The unit cell of a zeolite viewed along the a axis is shown below in which the framework of the zeolite is shaded black and the pores are shaded white. Plot the pore size distribution graph for this zeolite. Assume that no pores run through the solid in any direction other than that shown. (4 marks) (ii) Discuss, with examples, how the catalytic functionality of zeolites may be applied to combat societal environmental concerns. (4 marks) 3. Answer ALL parts. This question concerns the enzymatic synthesis of an amide from the two steps shown below. (a) Answer ALL parts (i) - (iii). (i) Identify biocatalysts A and B. (2 marks) (ii) Both biocatalysts have been shown to work under the same reaction conditions. State whether the reactions should be run together in a single vessel or as separate sequential steps. Justify your answer in terms of process requirements. (6 marks) (iii) Draw the structure of compounds 1, 2 and 3. (4 marks) (b) Answer ALL parts (i) - (iii). (i) Identify which of the following are involved in the mechanisms of biocatalysis by A and B respectively. (2 marks) (ii) Suggest an arrow pushing mechanism for EITHER the production of compound 1 (mediated by biocatalyst A) OR the production of the final amide and compound 3 (mediated by biocatalyst B). There is no need to include recycling of cofactors. (8 marks) (iii) Biocatalyst A was found to have low activity when tested with a ketone starting compound instead of the aldehyde shown above. Suggest why this might be the case and how the activity could be increased. (2 marks) (c) The reaction catalysed by biocatalyst B can be performed at room temperature in water. A traditional route to the same products requires areflux reaction in benzene in the presence of a ruthenium complex. Compare and contrast the biocatalytic and traditional chemical routes in terms of environmental sustainability. (6 marks)
Gameplay Programming (CT5GPROG) Module Code: M30849 Level: 5 General Assessment Information Assessment 1 (100%): Mechanics Demo and Presentation Submit online via Moodle. Check Moodle for due date. Short Description: The coursework is completed individually and consists of a video demonstrating and evaluating your artefact, while also reflecting on your progress in the module. You will also be required to submit the actual artefact, which will contribute to your mark. This assessment constitutes 100% of your marks for this module and will assess Learning Outcomes 1, 2, 3, and 4 of the module description. Second Attempt: Complete the assessment again, submitting a new or updated video presentation. Module Abstract, Learning Outcomes and Key Dates Gameplay Programming (CT5GPROG) Abstract This module introduces the practicalities of implementing gameplay for computer game prototypes. Through a mixture of code analysis and guided practical experimentation the students will gain familiarity with common programming patterns and gameplay mechanisms, and examples of approaches to implementing them in code. For their coursework they will be required to present small scale but nonetheless challenging artefacts, demonstrating appropriate implementation skills in suitable programming environments. Learning Outcomes On successful completion of this module, students should be able to: LO 1 Implement common gameplay mechanisms in code. LO 2 Utilise industry-relevant programming patterns in the creation of a software artefact. LO 3 Respond effectively to small-scale programming challenges. LO 4 Critically evaluate high-level options for game prototyping. Key Dates Assessment 1 (100%): Mechanics Demo and Presentation Submit online via Moodle. Check Moodle for due date. Assessment 1 Brief – 100 marks Your final deliverable for this module will consist of a small gameplay prototype and a 15-minute video presentation. The focus of the module and the assessment is gameplay programming, with particular emphasis on code quality, data structures, design patterns, and project structure. Your prototype must be inspired by the theme “All Dogs Go to Heaven”, the passion project of your studio’s new, eccentric creative director. They envision the game as a series of vignettes or small gameplay loops based on dog activities, aspects of a dog’s life, or how they might manifest in the afterlife. It is your job to prototype ideas for this larger project. You are free to interpret this theme broadly but remember that the focus should be programming the mechanics, rather than specific game design, visuals or animations. This means your “dog” could be a represented by a random game object. It shouldn’t be fully animated with complex physics, unless that is important to your prototype. This assessment constitutes 100% of your marks for this module and will assess Learning Outcomes 1, 2, 3, and 4 of the module description. The Task Create a small gameplay prototype based on the theme of dogs. Your prototype should include at least two distinct gameplay mechanics, one primary mechanic and at least one mechanic that supports this primary system. The interplay of these mechanics should create a small gameplay loop. Simple mechanics that facilitate interaction (e.g., simple movement of objects, keypresses etc.) do not count. Most importantly, the prototype should be designed so that it can be extended or integrated into a larger game project, with a focus on creating clean, modular, and maintainable code using good programming techniques. Simply creating a prototype with purely functional gameplay mechanics is not sufficient. Below are examples of types of mechanics that integrate some of the concepts covered in the module: o Primary mechanic: A system where the dog follows a scent trail through an environment. ● Play Interactions o Secondary mechanic: Sometimes, the dog doesn’t listen to specific commands and can be trained to ensure they perform. the correct command. o Primary mechanic: A system where the player manages a pack of dogs, each with different attributes and abilities. The leader can be switched out to access these different abilities to, for example, solve puzzles. ● AI Behaviours o Secondary mechanic: Environmental hazards that broadcast their status into the environment and affect the dog’s behaviour (e.g. birds, rocks) Programming Requirements Your gameplay prototype must demonstrate the application of complex programming approaches, such as the gameplay programming patterns and data structures discussed during the module. You may also incorporate other gameplay programming approaches from external research if this is discussed with your lecturer. You may want to incorporate one or a few relevant concepts from the module: ● Data Structures: Linked lists, queues, stacks, hash tables, and graphs. You may choose any engine, but it must support object-oriented programming (OOP), as this is essential for implementing good class structures and implementing specific gameplay programming patterns. ● Unreal Engine may also be used, but you must use C++ code, not Blueprints.
MODULE: NBS-6108A THE DYNAMIC ORGANISATION: PRACTICING ORGANISATIONAL DEVELOPMENT (2023/24) SUMMATIVE ASSESSMENT COURSEWORK 1. OVERVIEW Aim: this coursework is designed to give you the experience of applying organisational development (OD) frameworks, tools, and research in an applied business context and to demonstrate that you have met the module learning outcomes. Assignment Description: An investigation into the organisational development (OD) approach with reference to the TradeCo. case study. Assessment Method: Individual coursework in the form. of a written report. Assessment Weighting: This coursework counts for 100% of the assessment in this module. Submission deadline: 13th January 2025. Submission method: Blackboard. Word limit: 3,000 words maximum. The Executive summary, References and Appendices (such as analytical models taught on the module) do not count towards the word count. However, DO NOT use figures to replace text. Ensure your analysis is clearly described in the main body of your report. How the Formative Assessment Supports the Summative: The formative assessment provides an opportunity for you to familiarise yourself with the TradeCo case study and practice identifying the drivers of OD and OD mindset taken in the case study. 2. THE SUMMATIVE ASSIGNMENT Sam Stern, the Chief Executive Officer (CEO) of TradeCo has commissioned you to investigate why the Apex organisation development (OD) programme was not successful and make recommendations going forward. Your task is to analyse and evaluate the TradeCo case study using your knowledge of the practices and principles of Organisation Development taught on the module. You will be expected to critique the way OD was conducted at TradeCo. You are required to present your analysis of the case study and recommendations in a written report. You are required to apply the tools, frameworks and models taught and practiced on the module in your analysis and present this in the report. Guidance for completion of the report is set out in the sections below. Further detailed guidance will be provided in lecture and workshop 7 (Assessment Support). You will need to familiarise yourself with the case study, TradeCo. The Format and Structure of your Report The assignment should be presented in a written report format. You should use numbered headings and sub-headings as appropriate and ensure there is a clear structure to your work. Also, include a separate title page and references page. Ensure any figures (such as completed templates showing your analysis and using the practical tools from the module) are clearly labelled in an Appendices section and referred to in the report. Your report should contain the following sections: 1. Title page 2. Executive summary 3. Analysis of the drivers for OD at TradeCo. 4. Identification of the main mindset taken in the Apex OD programme in the case study (dialogic, diagnostic, or blended) 5. Analysis of how the Apex OD programme was undertaken with respect to why it failed. 6. Management recommendations to address the OD issues at TradeCo now (i.e., actionable interventions that TradeCo can implement based on your analysis) 7. Personal reflection 8. References 3. SUMMATIVE ASSESSMENT CRITERIA Your report will be assessed according to the following criteria: - Assessment Criteria To achieve good marks… Evidence of learning outcomes (20%) Task is fully addressed with learning outcomes met to a high standard. Argument, understanding and analysis (30%) Arguments, understanding, and analysis show understanding of and application of the course materials. Application of practical analytical tools (20%) Relevant analytical frameworks and analytical tools are identified and applied appropriately. Sources and evidence (10%) Arguments and analysis well supported by breadth and depth of academic and practice literature. Academic referencing (10%) Correct citation of sources throughout the report (including page numbers after quotations). Consistent, error-free, Harvard referencing at the end of the report Presentation and communication (10%) Clear, logical structure of the report that is communicated effectively You should familiarise yourself with: - The Marking Rubric for the coursework. This can be found in the Module Assessment Overview section of Blackboard. The rubric provides details of the standards required of the award of marks against each of the Assessment Criteria. The UEA Senate Scales Undergraduate – Coursework. These scales provide general guidance on academic standards required of the award of marks for this coursework. 4. GUIDANCE FOR COMPLETING THE ASSIGNMENT The following table sets out guidance for completing the section of your report, the most relevant lecture and relevant learning outcomes. Please note that the table is for guidance only. Detailed guidance on how to gain good marks will be provided in lecture and workshop 7 (Assessment guidance and support) Section of Report Description and details of the task Teaching sessions & Learning Outcome(s) 1. Executive Summary The Executive Summary is widely used in business and provide an ‘at a glance’ overview for business leaders. It should briefly summarise your analysis and recommendations. · Briefly set the scene, including introducing the topic of the report, why it is important, and how the report is organised. · Briefly summarise your findings and recommendations -Assessment support & guidance (7) LO1 2. Analysis of the drivers for Organisation Development in TradeCo · Identify and explain the external and internal context of TradeCo (i.e., the drivers for OD up to the present time) and the implications for OD at TradeCo at the present time. -Introduction (1) -Why develop? (2) LO1 3. Analysis of whether TradeCo adopted a diagnostic, dialogic or blended approach · Identify and explain the main OD mindset taken in the Apex OD programme by applying your understanding of diagnostic and dialogic OD. · Identify and critically evaluate the advantages and disadvantages of this approach in the case study. · Support your analysis with reference to relevant literature. -Why develop? (2) -Appreciative Inquiry (3) -Diagnostic & dialogic mindsets (4) -Processes of OD (5) LO2 LO3 4. Analysis of how organisation development was undertaken at TradeCo · Analyse and critically evaluate the strengths and weaknesses of the Apex OD programme: - How TradeCo gained an understanding of the challenges and issues. - The interventions TradeCo put in place. - TradeCo’s evaluation approach. - Use relevant tools and techniques taught on the module in your analysis and incorporate both dialogic and diagnostic OD · Support your analysis with reference to relevant literature and the application of relevant tools taught on the module. Understanding issues (6) -Designing Interventions (8&9) -Evaluation (10) LO4 LO5 LO6 5. Management Recommendations · Using your analysis in the preceding section, make recommendations for how TradeCo could improve its OD programme, applying both dialogic and diagnostic mindsets: - Your understanding of the challenges and issues at TradeCo. - Actionable interventions to address the issues at TradeCo. - How to evaluate your recommendations and interventions. · Support your analysis with reference to relevant literature and the application of relevant tools taught on the module. -As above -Insights online lectures and independent research LO4 LO5 LO6 6. Personal reflection Reflect on what you have learned from the module and case study analysis, in terms of knowledge, skills and attitudes. What areas of practitioner skills could you look to develop further and how will you take this learning into your first job? -Introduction LO1 Key to the Learning Outcomes (see Module Outline) LO1: Understand the principles of OD and identify when they might be applicable LO2: Understand the diagnostic and dialogic approaches to OD, identify the advantages of each, how they may be complementary. LO3: Explain the processes of OD when applying dialogic and diagnostic approaches LO4: Apply and critically evaluate frameworks and tools to understand issues and opportunities for development LO5: Analyse organisational cases in order to identify a range of recommendations for interventions for improvement, using relevant frameworks LO6: Apply relevant principles and frameworks to evaluate the effectiveness of OD. ‘How much should I write’? Note: this is rough guidance only and assumes 500 words per page. · The executive summary should be no longer than 0.5 page. This does not count in the word limit · Analysis of the drivers of OD and main OD mindset in the case study - about 1 page (500 words). · Analysis of the Apex OD programme - about 2.5 pages (1250 words). · Management recommendations – about 2 pages (1000 words) · Personal reflection about 0.5 page (250 words). 5. COURSEWORK SUPPORT To assist students in preparing the coursework, the following support is provided during the module. · Formative assessment – you are provided with the opportunity to practice tasks and skills fundamental to success in the coursework · Coursework lectures (lecture 7 & workshop 7) – there is an interactive lecture and workshop with examples and activities to increase understanding of the coursework tasks; and how to maximise marks. · Workshops – the workshops provide interactive tasks to build skills for the coursework · Blackboard Discussion Forum – you can post queries about the coursework for reply by the module organiser and read responses to other student’s questions. · Drop-in Q&A sessions – week commencing 9th December, there are drop-in sessions for assessment support. 6. REASSESSMENT If reassessment is required, you will be notified by the UG LTS Hub. A similar assignment, using different case study will be set.
PHYS4035 MACHINE LEARNING IN SCIENCE PART 1 (2024) Project and Paper You will work in groups of three or two (due to numbers and preferences) on a research project based on your preferences on selected aspects of ML. The outcome of the project will be a short paper describing the project and results in the style of a research journal article. The project allocations and descriptions are given below. Paper: The project description and results will be presented in a 3 page paper (2 pages for two-member group) in the style of Physical Review Letters, ideally typeset in LaTeX (for example via the RevTeX package) although we will accept other modes of preparation compatible with a two column PRL format and style. The paper will have the usual structure (title/authors/affiliations/abstract in the heading section, followed by the main text, figures with appropriate captions, standalone equations, and references). Do not include code in the paper (only pseudo-code if necessary to describe an algorithm). Groups doing projects that require coding: All programming will be done in python (version 3). Use of neural network packages (Keras,TensorFlow, etc.) is not allowed; use of other standard packages (numpy, scipy, etc.) is allowed. The code will be submitted together with the paper. The readability of the code will form. part of the assessment. (NB: both paper and code will go through plagiarism detection system.) Schedule and deadline: Projects will be announced by Nov 28th (during the RL-W3 class). There are no online workshops after that week to allow time for project work. The deadline for handing in the paper is February 3rd 2025 3pm (*new deadline*) (submission via Moodle). Assessment: The mark for the Project and Paper contributes 60% to the overall mark for the module. The assessment has two components Quality and style. of presentation = 50% Content and understanding = 50% Project groups (An = CNeuro, Bn and Cn = MLiS): Group A1, Project P1: Johnson, Thomas-Roche, Undelikwo Group A2, Project P2: Carr, Gunel, Srinivasan Group A4, Project P4: Broadhurst, Kuntipalo, Su Group A6, Project P6: Condon, Mathias, Oliver Group A9, Project P9: Goldsmith, Hardy, Mahmoud Group B1, Project P1: Pan, Xuan, Zhao Group B2, Project P2: Barrick, Chongsawad, Patil Group B4, Project P4: Jadhav, Li, Tam Group B6, Project P6: Liu, Shen, Yan Group B8, Project P8: Gamston, Rudd, Underdown Group B9, Project P9: Atkinson, Marshall, Rolfe Group C1, Project P1: Chandran, Kuatbekov, Lahane Project descriptions. Here we provide a brief description of the projects. These are meant to set the overall topic and a basic specification of the problem to study. We expect students to research the topic and come up with a suitable project to pursue along the proposed lines. P1 Breast Cancer Tumours (UL) The data for the Breast Tumours is available from the ML repository of UC Irvine https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+ original under Breast Cancer Wisconsin (Original) The goal of the project is to build an understanding of the difference in features for malignant and benign tumours, using unsupervised learning methods, based on the features available in the above data set. P2 Butterfly Species Richness (UL) Data for the Butterfly Species Richness is provided in the file ButterflyData.ods available in Moodle. The goal is to understand the distribution of butterfly species richness from an USL perspective. In particular, understanding what features are important for determining the distribution. Using the location name, one can create a large number of features, and therefore a very high dimensional feature space. Unsupervised learning methods can be used reduce the dimensionality of this space, and therefore understand the correlations between these features and butterfly richness. Examples of techniques might include, PCA or autoencoders. P3 Artificial vs Neurological Learning (theoretical) Address the question: Is it necessary to re-learn networks with every task variant, or can we create a smarter network? Use the following reference as a starting point, J.X. Wang, “ Prefrontal cortex as a meta-reinforcement learning system”, Nature Neuroscience 21, 860 (2018). P4 Classification of Breast Cancer Tumours (SL) The data for the Breast Tumours is available from the ML respository of UC Irvine https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+or iginal under Breast Cancer Wisconsin (Original) The goal is to build a model to predict whether a given tumour is malignant or benign, based on the features available in the above data set. P5 Artificial vs Neurological Learning (theoretical) Address the question: How do deep networks for image recognition compare to the mammalian visual system? Use the following reference as a starting point, D.L.K. Yamins et al., “Performance-optimized hierarchical models predict neural responses in higher visual cortex”, PNAS 111, 8619 (2014). P6 Changes to Arctic Ice Extent (SL) The Arctic Ice Extent data is available at https://noaadata.apps.nsidc.org/NOAA/G02135/north/monthly/data/ The goal is to build a model to predict the Arctic ice extent and, more ambitiously, the first time (if any) when the Arctic will be ice-free. Potential Ideas and Approaches: Can start by simply building a linear regression model to predict for the year-averaged Arctic Ice extent with the year as input. Can continue development by finding more features to use as inputs, such as the global mean temperature or CO2 concentration, or using time-series approaches (predicting the next year’s extent based on previous extents). Can also try to model seasonality by including the monthly extent data, and predict the first ice-free year. P7 Reinforcement Learning in Neurological Systems (theoretical) Explore how much reinforcement learning in ML relates and is inspired by related problems in biological systems. A good starting point is this classic paper on reward signals in the brain. W. Schultz, P. Dayan, and P.R. Montague, “A neural substrate of prediction and reward”, Science 275, 1593-1599 (1997). P8 Reinforcement Learning of (stylised) Blackjack (RL) The aim of this project is to train an agent to play the card game Blackjack, in an environment that can have a varying degree of complexity. For simplicity, we consider a stylised version of the game where there is no opponent. For details see description in Moodle. P9 Controlling a Drone via Reinforcement Learning (RL) The aim of this project is to control an idealised drone that moves in two dimensions by means of reinforcement learning. For details see description in Moodle.
APH402 Homework 2 Due on Monday, December 23, 2024 Question 1. Give your understanding on the following: 1) Mass Spectrometry. [10 marks] 2) Major components of Mass Spectrometers and their functions. [10 marks] 3) The working principles of EI, ESI, MALDI, QQQ, and ToF. [50 marks] 4) Applications, advantages and disadvantages of Mass Spectrometry. [30 marks] Question 2. Read the reference paper attached (L. Qu et al. Fitoterapia 78 (2007) 200–204.) and answer the following questions (Caution,there maybe some typo in the paper!): 1) Draw the chemical structures of the six isoflavones from Dandouchi; [6 marks] 2) Explain why the crude extract can be separated into two fractions by varying the concentration of ethanol in the mobile phase; [10 marks] 3) Explain difference of mobile phases used for the analysis of Fraction 1 and 2; [6 marks] 4) Explain the retention order of the compounds in the two fractions; [12 marks] 5) What is the function of 2% AcOH in the mobile phases? [8 marks] Question 3. An unknown compound, C13H18O2, has only one strong IR absorption peaks at 1741 cm-1. The 1H and 13C{1H} NMR, COSY, HSQC, and HMBC spectra of the compound recorded in CDCl3 at 298 K and 500 MHz are given below. 1) Derive the structure of the compound by using the data provided. For each 2D spectrum, indicate which correlation gives rise to each cross-peak by placing an appropriate label in the box provided (e.g. H1 → H2, H1 → C1) on the diagrams and submit the diagrams together with your answer booklet. [30 marks] 2) List 3 important peaks that would show in the mass spectrum of the compound. [6 marks] 1H NMR spectrum (CDCl3, 500 MHz) 13C{1H} NMR spectrum (CDCl3, 125 MHz) 1H–1H COSY spectrum (CDCl3, 500 MHz) 1H-13C HSQC spectrum (CDCl3, 500 MHz) 1H-13C HMBC spectrum (CDCl3, 500 MHz) Question 4. An unknown compound, C10H12O3, has only one strong IR absorption peaks at 1720 cm-1. The 1H and 13C{1H} NMR, COSY, HSQC, and HMBC spectra of the compound recorded in CDCl3 at 298 K and 500 MHz are given below. Derive the structure of the compound by using the data provided. For each 2D spectrum, indicate which correlation gives rise to each cross-peak by placing an appropriate label in the box provided (e.g. H1 → H2, H1 → C1) on the diagrams and submit the diagrams together with your answer booklet. [30 marks] 1H NMR spectrum (CDCl3, 500 MHz) 13C{1H} NMR spectrum (CDCl3, 125 MHz) 1H-1H COSY spectrum (CDCl3, 500 MHz) 1H-13C HSQC spectrum (CDCl3, 500 MHz) 1H-13C HMBC spectrum (CDCl3, 500 MHz)
