# multiple regression project

At least five quantitative variables, but not more than 8 variables total. The minimum number of cases is 40 for this exercise.

Description of criterion
Intro - General Explanation
Intro - Explain what drew you to this topic
Data: Response/Dependent Variable
Data: Independent Variables (at least 4, not more than 7)
Data: minimal of 40 cases
Data: if categorical variable, modeled correctly
Regression Model: Create a regression model (first try)
Regression Model: Interpret model equation
Regression Model: Significance of Independent Variables
Regression Model: Check model assumptions and make appropriate comments
Regression Model: How good is model; R square, R square alpha, Standard error, F-value, alpha of F value, etc
Refine Model: Does an interaction term create a better model?
Refine Model: Can a variable be eliminated
Refine Model: Stepwise regression improve model? Comment and substantiate.
Refine Model: Forward selection regression improve model? Comment and substantiate.
Refine Model: Backward elimination regression improve model? Comment and substantiate.
Forecast and Predict using Chosen Model: Identify chosen model and mean value of Y and chosen values of Xs.
Forecast and Predict using Chosen Model: 95% CI for mean value of Y for given values of Xs.
Forecast and Predict using Chosen Model: 95% PI for value of Y for given values of Xs.
Forecast and Predict using Chosen Model: Compare results for CI and PI; comment on similarities and differences of intervals.
Forecast and Predict using Chosen Model: Identify outliers and influential observations. Discuss how this affects results.
Conclusions: are you comfortable with the model?
Conclusions: If you needed to rebuild the model, what changes would you consider.
Appendix: Copy of data set; legible and embedded in word document.
Appendix: Meta-data of set; who, what, when, where, why Plus variable list with variable label in data file, coding of variable (metrics or coding or dummy coding.)
Appendix: Figures, tables, outputs of analysis using software. Clearly label and identify. Refer to them in discussion.

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