P.S. I am attaching the screenshorts with my graphs and the table itself in case the writer needs to look closely at the chart. 1.Run the Linear Regressions Select the variables from the data set in SPSS that you think will affect the attendance and that make the most sense. Hint: Use attendance as the dependent variable. Select your independent variables carefully. Hint: If you include the days of the week and/or the months of the year as independent variables, make sure you exclude one day of the week and one month of the year from your independent variables. (Otherwise, you may run into problems with the full rank assumption. SPSS will automatically exclude any variables that will cause full rank problems, but it is better to exclude them yourself.) Run various linear regressions and compare the adjusted R squares with one another. The goal of the regression is to get the adjusted R square as high as possible, but be careful not to run into multicollinearity issues. Report the results for the model with the highest R square. Report Requirements Your report must be 2 pages and include the following: the Coefficients table that SPSS provides in the output, the Model Summary table that includes the adjusted R square, the exact steps you took to arrive at the final results, any problems or challenges you encountered, and your reasons for excluding certain variables because of outliers or multicollinearity problems (if applicable).
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