The main purpose of this assignment is for you to develop a case study and a write-up that tells a compelling business data-analytics story using the PPDAC framework. It should present a data analysis that provides a clearly explained answer to a question to help inform and improve business decision-making. You should therefore choose a question that is (a) decision-relevant; and (b) answerable using available data. Examples of possible case study topics include the following:
• Identify what independent variables an outcome of interest depends on (e.g., a financial impact or cost-effectiveness measure, service quality rating, employee engagement, process metric, or other KPI).
• Test null hypotheses that specified outcome variable(s) are independent of one or more independent variables.
• Quantify how well the values of some variables can be predicted from the values of others (e.g., using the R2 of a regression model).
• Compare different predictive models (e.g., Multiple Linear Regression (MLR) using lm() vs. CART using rpart() vs. random forest or other machine-learning (ML) models) to determine which provides the most accurate predictions for an outcome of interest.
• Use Markov models, Poisson regression, or other appropriate probability models to predict how different members of a population receiving nudges (e.g., to enroll in a program, change a behavior, adopt a new product or service, remember to make payments, etc.) will respond over time.
• Use forecasting models to predict diffusion of a new technology or product over time, and to predict how different introduction and marketing strategies may affect adoption rates.
You should formulate an interesting question that can be addressed through analysis of one or more data sets (the first P step in PPDAC).
• Your problem statement should explain the underlying question or issue and why it matters (what is at stake, what decisions need to be made that the data analysis will inform).
• State how the question will be addressed by testing one or more null hypotheses or by estimating one or more quantities from data (covered in planning, the second P in the PPDAC cycle).
o Common null hypotheses to test are that
2 quantities are independent (e.g., that purchase probability does not depend on customer age; or that sales volume does not depend on advertising expenditure);
2 or more quantities have the same distribution (e.g., that men and women have the same admission probability to a program); or
The empirical distribution of a quantity matches a specified theoretical distribution or has specified properties (e.g., that sales volumes are normally distributed, or that the mean change in customer satisfaction from before to after an information campaign is 0).
If the null hypothesis of independence is rejected, then it remains to describe how one variable depends on others.
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