# Statistical Regression Analys

The main goal of the final project is to apply the concepts that you have learned throughout the course to real-world data. In this project first you need to explore the data through descriptive statistics and graphical summaries and then use multiple regression analysis to analyze the relationships between variables. The data set provided to you includes information on homes on the market by North Valley Real Estate.

The initial report for the project should be a 2-4 page paper (this does not include computer output/tables/graphs) that describes the details of each variable (types of the variable, summary statistics: mean, median, standard deviation, etc.) and shows the variable’s distribution using histograms or frequency polygons (or other graphical summary methods discussed in week 1).

The final report for the project should be a 6-10 page paper (this does not include computer output/tables/graphs) that describes the question of interest, how you used the data set to analyze the question with details on the steps you used in your analysis, your findings about the question of interest and the limitations of your study. Specifically, your report should contain the following:

1. Abstract: includes a one paragraph summary of what you set out to learn, and what you ended up finding. It should summarize the entire report.

• Introduction: includes a brief introduction about the data, a discussion of the question of interest: What properties of a home are related to its selling price on the market?

A brief overview of your methodology used to examine the research question, a summary of the results of your study, and an outline of the remaining organization of the paper.

3. Data Set: includes details about the variables in the data set, summary statistics, and visual tools to show the data (e.g. box plots, histograms, scatter graphs)

Note: You can include your initial report for this section.

4. Methodology and Results: includes testing if data meets the assumptions of regression (such as not correlated independent variables, linearity, and etc.), running the multiple regressions, using stepwise regression methodology to find the best model, providing inferences about the question of interest, and writing a detailed interpretation of the regression results (such as interpretation of the coefficients, ANOVA table, t tests, p-values, coefficient of determination, etc.) and discussion.

5. Limitations of study and conclusion: includes describing any limitations of your study and how they might be overcome in future research and provide brief conclusions about the results of your study.

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