Individual Assignment 2 2023 (1)

Assessment (non-exam) Brief Module code/name MSIN0093: Business Strategy and Analytics Module leader name Dr. Anil Doshi Academic year 2023/24 Term Assessment title Individual Assignment 2 Individual/group assessment Submission deadlines: Students should submit all work by the published deadline date and time. Students experiencing sudden or unexpected events beyond your control which impact your ability to complete assessed work by the set deadlines may request mitigation via the extenuating circumstances procedure . Students with disabilities or ongoing, long-term conditions should explore a Summary of Reasonable Adjustments . Return and status of marked assessments: Students should expect to receive feedback within one calendar month of the submission deadline, as per UCL guidelines. The module team will update you if there are delays through unforeseen circumstances (e.g. ill health). All results when first published are provisional until confirmed by the Examination Board. Copyright Note to students: Copyright of this assessment brief is with UCL and the module leader(s) named above. If this brief draws upon work by third parties (e.g. Case Study publishers) such third parties also hold copyright. It must not be copied, reproduced, transferred, distributed, leased, licensed or shared with any other individual(s) and/or organisations, including web-based organisations, without permission of the copyright holder(s) at any point in time. Academic Misconduct: Academic Misconduct is defined as any action or attempted action that may result in a student obtaining an unfair academic advantage. Academic misconduct includes plagiarism, obtaining help from/sharing work with others be they individuals and/or organisations or any other form of cheating. Refer to Academic Manual Chapter 6, Section 9: Student Academic Misconduct Procedure - 9.2 Definitions . Referencing: You must reference and provide full citation for ALL sources used, including AI sources, articles, text books, lecture slides and module materials. This includes any direct quotes and paraphrased text. If in doubt, reference it. If you need further guidance on referencing please see UCL’s referencing tutorial for students . Failure to cite references correctly may result in your work being referred to the Academic Misconduct Panel. Use of Artificial Intelligence (AI) Tools in your Assessment: Your module leader will explain to you if and how AI tools can be used to support your assessment. In some assessments, the use of generative AI is not permitted at all. In others, AI may be used in an assistive role which means students are permitted to use AI tools to support the development of specific skills required for the assessment as specified by the module leader. In others, the use of AI tools may be an integral component of the assessment; in these cases the assessment will provide an opportunity to demonstrate effective and responsible use of AI. See page 3 of this brief to check which category use of AI falls into for this assessment. Students should refer to the UCL guidance on acknowledging use of AI and referencing AI . Failure to correctly reference use of AI in assessments may result in students being reported via the Academic Misconduct procedure. Refer to the section of the UCL Assessment success guide on Engaging with AI in your education and assessment . For staff reference only: template version 1.0 September 2023

Content of this assessment brief Section Content A Core information B Coursework brief and requirements C Module learning outcomes covered in this assessment D Groupwork instructions (if applicable) E How your work is assessed F Additional information

Section B: Assessment Brief and Requirements Please complete each problem in the assignment. Any additional criteria specific to this assessment are detailed in section F. 1. [300 words, Week 4,5] IdeaWeb follow-up analysis. Read the IdeaWeb (C) case attached (ignore the case questions). Say Duran showed you these two regressions. State three interesting observations from the regression results. For each observation, refer to one or more specific coefficients. You do not need to analyse every coefficient in the regressions. The idea of this question is for you to explain what you see as the most interesting or important results. 2. [700 words, Week 6] Experiment proposal. You have joined the data science team of the company of your dreams. (Choose a well-known firm such as McDonald's or Alibaba. Indicate the company in the first sentence of your answer.) The head of the data science team gives you your first assignment: “At our company, we depend on experimentation to find ways to improve performance. I would like you to propose and design an experiment to test some strategic aspect of our business. Your proposal can be related to the company's user interface, products, customer experience, or another area of the company.” Draft a proposal for an experiment. (You do not need to run the experiment and you do not need any data for this problem.) Your response should include the following elements: a. Identification of the aspect of the business or operations that you would like to test and its strategic relevance. b. A description of the overall evaluation criterion (OEC) you will use and a brief justification of the choice. c. A description of the current implementation (i.e., the control) and your proposed intervention (i.e., the treatment). Ensure that your proposed intervention is clearly described. You are encouraged to be creative in your description (verbal descriptions, sketches, screenshots, etc. are all acceptable). Design your experiment with only one treatment. d. What is the population of interest? Describe how you would randomly assign subjects of that population into the treatment and control. e. The null and alternative hypotheses. f. What are the policy implications for your company if the null hypothesis is rejected? What are the implications if the null hypothesis is not rejected?

3. [300 words, Week 7]. Causal diagrams in real estate. You work as a data analyst at a real estate company. Management has asked you to assess whether improvements in landscaping cause increases in home values. You decide to sketch a causal diagram. Your causal diagram includes the following variables:  landscaping quality (independent variable of interest)  home value (dependent variable of interest)  house age  lot size  outdoor space functionality (how the outdoor space can be used) a. [Image] Assume you run the following regression to show the causal effect of “landscaping quality” on “home value:” lot ¿¿ value = β 0 + β 1 ( landscapingquality ) + β 2 ( year built )+ β 3 ( outdoor space functionality )+ β 4 ¿ Neatly sketch the causal diagram that implies this regression identifies the causal relationship of interest. b. [1-2 sentences] Does your causal diagram include an arrow from “lot size” to “outdoor space functionality”? Why or why not? c. [1-2 sentences] Does your causal diagram include an arrow from “outdoor space functionality” to “lot size”? Why or why not? d. [1-2 sentences] Propose one way you might measure “landscaping quality.” e. [Brief paragraph] Even if your causal diagram is correct, state two reasons why your regression might not estimate the true causal relationship of “landscaping quality” on “home value?”

be the same for the other regional offices? Table 1: Software Roll Out Variable Descriptions Variable Description agent id an anonymized agent identifier avg timeoncall the average number of minutes spent on calls during the week got software =1 if the agent was using the new software that week, =0 otherwise weeknum the week of the data period (ranges from -3 to 4; the software rolled out between weeks 0 and 1) mentor hours number of hours spent mentoring other agents in the prior week Female =1 if agent identifies as female, =0 otherwise Table 2: Regression Results DV: avg timeoncall (1) (2) got software –2.018*** –1.518*** (0.059) (0.007) mentor hours –0.105*** –0.095*** (0.003) (0.011) female [dropped] [dropped] constant 16.937*** 17.426*** (1.669) (1.845) Agent fixed effects? Yes Yes Week dummies included? Yes Yes Agent-weeks (num obs) 3928 7392 Number of agents 491 924 F-Stat 69.7 74.2 Adj R-squared 0.44 0.48 5. [250 words, Week 10]. Data ethics. Answer the following questions about different types of data ethical considerations: a. [1-2 sentences] Why might bias in sampling lead to ethical concerns? b. [1-2 sentences] Why might bias in variables perpetuate historical outcomes? c. [1-2 sentences] Why might collecting data for one purpose and using it for a different purpose lead to ethical concerns?

Section C: Module Learning Outcomes covered in this Assessment This assessment contributes towards the achievement of the following stated module Learning Outcomes as highlighted below:  Apply economic principles to incorporate data and data analytics into a company's value proposition  Consider the ethical implications of data applications  Design surveys and experiments and analyze the resulting data  Apply methods on observational data to arrive at causal inferences

Section E: How your work is assessed Within each section of this assessment you may be assessed on the following aspects, as applicable and appropriate to this assessment, and should thus consider these aspects when fulfilling the requirements of each section:  The accuracy of any calculations required.  The strengths and quality of your overall analysis and evaluation;  Appropriate use of relevant theoretical models, concepts and frameworks;  The rationale and evidence that you provide in support of your arguments;  The credibility and viability of the evidenced conclusions/recommendations/plans of action you put forward;  Structure and coherence of your considerations and reports;  Appropriate and relevant use of, as and where relevant and appropriate, real world examples, academic materials and referenced sources. Any references should use either the Harvard OR Vancouver referencing system (see References, Citations and Avoiding Plagiarism )  Academic judgement regarding the blend of scope, thrust and communication of ideas, contentions, evidence, knowledge, arguments, conclusions.  Each assessment requirement(s) has allocated marks/weightings. Student submissions are reviewed/scrutinised by an internal assessor and are available to an External Examiner for further review/scrutiny before consideration by the relevant Examination Board. It is not uncommon for some students to feel that their submissions deserve higher marks (irrespective of whether they actually deserve higher marks). To help you assess the relative strengths and weaknesses of your submission please refer to SOM Assessment Criteria Guidelines , located on the Assessment tab of the SOM Student Information Centre Moodle site. The above is an important link as it specifies the criteria for attaining the pass/fail bandings shown below: At UG Levels 4, 5 and 6: 80% to 100%: Outstanding Pass - 1st; 70% to 79%: Excellent Pass - 1st; 60%-69%: Very Good Pass - 2.1; 50% to 59%: Good Pass - 2.2; 40% to 49%: Satisfactory Pass - 3rd; 20% to 39%: Insufficient to Pass - Fail; 0% to 19%: Poor and Insufficient to Pass - Fail. At PG Level 7: 86% to 100%: Outstanding Pass - Distinction; 70% to 85%: Excellent Pass - Distinction; 60%-69%: Good Pass - Merit; 50% to 59%: Satisfactory - Pass; 40% to 49%: Insufficient to Pass - Fail; 0% to 39%: Poor and Insufficient to Pass - Fail. You are strongly advised to review these criteria before you start your work and during your work, and before you submit. You are strongly advised to not compare your mark with marks of other submissions from your student colleagues. Each submission has its own range of characteristics which differ from others in terms of breadth, scope, depth, insights, and subtleties and nuances. On the surface one submission may appear to be similar to another but invariably, digging beneath the surface reveals a range of differing characteristics. Students who wish to request a review of a decision made by the Board of Examiners should refer to the UCL Academic Appeals Procedure , taking note of the acceptable grounds for such appeals. Note that the purpose of this procedure is not to dispute academic judgement – it is to ensure correct application of UCL’s regulations and procedures. The appeals process is evidence-based and circumstances must be supported by independent evidence.