STAT4610Project4

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University Of Denver *

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4610

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Statistics

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Jun 12, 2024

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STAT4610 Project 4 Multiple Regression Model and Business Report For this Project you will collect data, perform preliminary data analysis, build and analyze a model, and use the results of your analysis to make predictions, draw conclusions, and support decisions. The Project will be submitted in two phases: Phase I : Collect data and describe your data set. Perform preliminary analysis of your data, using descriptive statistics [0 points, but corrections not made for Phase II will result in a deduction, as will late or missing submissions]. (Due before class, Lesson 13.) Phase II : Build a multiple regression model from your data, and prepare a business report that includes all of your previous work, and that presents a recommendation to a decision-maker based on your model and analysis [250 points]. (Word Doc or pdf due thru CANVAS at the beginning of class, Lesson 15.) PROJECT DETAILS Phase I, Data Collection and Preliminary Data Analysis You may collect your data from (almost) any source(s). The objective is to find a numerical response (dependent) variable that can be predicted from some number of other (independent) variables. (The independent variables may be categorical, binary, or numerical, but the response variable must be numerical.) These data do not have to come from the same source, but should be compatible as data sets. Data should be cross-sectional (no time-series data). The minimum requirement is 50 observations with ten independent variables. [Beware the tautology: do not collect temperature and humidity to “predict” the heat index!] Ensure that your data set will allow you to draw relevant conclusions about something that matters. The data may be from any field (preferably business-related) and should be collected so that you can establish relationships among your data to support some sort of a conclusion or recommendation. Apply descriptive statistics to your data set. This should include graphical depictions (histograms and scatter plots) as well as some basic calculated descriptive statistics. Since you will be building a multivariate model, the correlations between your independent variables should be included. You should, at this point, be able to make some preliminary observations about your data. These observations (and any others you come up with later) should make their way into your business report, but will generally appear in appendices unless you determine them to be critical to the decision you are recommending. This submission should be a word document (for the descriptive stats) and an excel file (for the data). Phase II: Model Construction and Business Report Build a multiple regression model from your data using the techniques we have learned in the course. You should decide here how you intend to use your model to conduct analysis, make predictions, and support decisions. Wrap all of your work up in a business report. Remember that the target is an executive who you will ask for a decision based on your recommendation. Perform analysis with your model, interpret your model, include your calculations and the original data (in appendices) but present the bottom line to the decision- maker up front. While many organizations suggest a format for a business report, there are as many that do not, so the presentation is up to you. However, the following page may be used as a guideline.

P1. Cover page  Title  For who?  Name P2. Executive summary (do not include this in P3)  Roughly half page – this what you need to know P3.Title  Bottom line up front – recommendation/decision  This is based on regression model  See appendix A for model analysis  This model is great or not (see appendix B) – I’m at least 95% confident…  We build this model through a process of… (see appendix C)  Data comes (See appendix D – data analysis)  Data source (See appendix E) Appendix A  Model  Coeff analysis  Impact analysis Appendix B – Statistical analysis  Regression output  Residual output Appendix C – modeling process Appendix D – data analysis Appendix E –  Dictionary  Cleaning Sleep Quality = 6.26 + 0.28 Gender (Higher for Females) - 0.14 Sleep Disorder - 0.54 Obese - 0.65 Overweight - 0.13* High Blood Pressure + 0.056 Age (Increases with Age initially) + 0.004 Physical Activity Level - 0.44 Stress Level (Strongest negative factor) – 0. 001532277 Heart Rate (Not statistically significant) + 5.30712E-05 Daily Steps + 0.12Sleep Duration

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