Simple and Multiple Linear Regression:

Use SAS On Demand for Academics BigClass.sas7bdat or Big_Class.csv for upload and import Tasks>>Linear Models >> Linear Regression Variables weight DV & height - IV (age, height, and sex for multiple regression) Use Effect Coding on multiple regression Model Effects height (age, sex, height, and sex*height for multiple regression) Options Diagnostics Predicted Values Collinearity Variance Inflation Factors

Submit a powerpoint telling the story of the weight variable and the predictors you tested above.

I used SAS on demand to complete the exercise where I developed linear and multiple regression models for the BigClass. Please see the attached tables and graphs for more details. However, I am struggling with how to best interpret the weight variable and the predictors I tested.

Can you help me to tell a compelling story about these variables?

Simple Linear Regression:

Multiple Linear Regression:

Model: MODEL1 Dependent Varlable: welght Results: Linear Regression.ctk Observed by Predicted for weight Results: Linear Regression.ctk 1:31 AM Results: Linear Regression.ctk Residuals for weight 1:31 AM Results: Linear Regression.ctk Results: Linear Regression Least Squares Model (No Selection) ASStudio/sasexec/submissions/847bd599-e109-49bd-9d9f-382a3947ac41/results Results: Linear Regression Observed by Predicted for weight 1:29AM Resuts: Linear Puegression Fit Diagnostics for weight \begin{tabular}{lr} \hline Cbservations & 40 \\ Parameters & 5 \\ Error DF & 35 \\ MSE & 259.81 \\ R-Square & 0.527 \\ Ad R-Square & 0.4729 \end{tabular} 1:29 AM Riesuls: Linear Regression Residual by Regressors for weight