Background

Using both the response variable (YELLOW column) and the predictor variable (DARK ORANGE column) develop appropriate descriptive summary measures reviewed in Module 1.

Simple RegressionModeling Steps

1. Open the Excel worksheet containing your Team Project Data.
2. As you learned in Module 2, run a simple linear regression model using the independent numerical variable whose Excel worksheet column is highlighted in DARK ORANGE as the predictor ofYthe YELLOW Excel column.
   1. Cut and paste into your report the scatter plot and the Excel Regression Output for this simple linear regression model.
   2. Write the sample regression equation.
   3. Interpret the meaning of the Y-intercept and slope for your fitted model.
   4. Interpret the meaning of the coefficient of determinationr².
   5. Interpret the meaning of the standard error of the estimate SYX.
   6. Obtain the residual plots and cut and paste them into the report. Briefly comment on the appropriateness of your fitted model.
      • (1) If the assumptions are met and the fitted model is appropriate,continueto Step 2G.
      • (2) If any of the linearity, normality, or equality of variance assumptions are problematic state this but continue to Step 2G. Note -- you do not need to check the assumption of independence in your project. (That assumption is automatically met because your project is not time-dependent).
   7. Comment on the statistical significance of your fitted model
   8. Comment on the 95% confidence interval estimate on the average impact on the response variable for each unit increase in the predictor variable.
   9. Select a value for your independent variable in its relevant range:
3. Predict ?

\f