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I have a data set file in the minitab file format. It is the data for the real estate .this is my stats class project work. Using all the predictor variables in data file build and select an appropriate regression model following model building steps appearing at the end of the Chapter 15 powerpoints.I can provide the model building steps. The appendix of exam project report should include printouts of the various stepwise and best subsets procedures and then, for the final model you select, a complete regression analysis with checks of the regression assumptions. Your project report should start (INTRODUCTION) with a statement of what your study is about and what potential predictor variables were initially being used to try to predict your dependent variable Y. In the BODY of the report mention any variables that had to be removed from the potential list of predictors because of collinearity. Then discuss any differences in the model selection process given by using the three stepwise methods and three best subsets methods. Based on this discussion name the particular model variables you have chosen, specify the reasons why and write the regression equation for it. Run that model in Minitab and completely review the assumptions and the significance of the overall model and each contributing prediction variable. You should be able to state what the intercept is and each of the slopes should be defined. Similarly list and define the adjusted rsquare and the standard error of estimate. Be sure to have entered a particular value for X if you select a simple regression or a set of values for the different predictors in a multiple regression that are all within the relevant ranges of the X value or values chosen. This way, in the SUMMARY section of your report your final printout will have the 95% confidence interval estimate of the mean response for all Y with the particular value(s) for X (or Xs) as well as the 95% prediction interval for an individual Y response and you will be able to describe these results which would be the purpose of building a regression model in the first place.

 

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