Determine the 95% prediction interval estimate of for an individual occasion when the independent variable has the particular value you selected.

Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variable has the particular value you selected.

Select a value for your independent variable in its relevant range:.

( Note: Every team should have a fitted model that is statistically significant so contact me immediately if this is not so).

Comment on the statistical significance of your fitted model.

If the equality of variance assumption appears to be seriously violated contact me.

You do not need to check the assumption of independence in your project – that assumption is met.

If the linearity or normality assumptions are problematic state this but continue to Step 2G with caution.

If the assumptions are met and the fitted model is appropriate continue to Step 2G.

Briefly comment on the appropriateness of your fitted model.

Obtain the residual plots and cut and paste them into the report.

Interpret the meaning of the standard error of the estimate.

Interpret the meaning of the coefficient of determination.

Interpret the meaning of the intercept and slope for your fitted model.

Cut and paste into your report the scatter plot and the Minitab Express printout for this simple linear regression model.

As you learned in class in Week 2, first develop a simple linear regression model using one of the above predictors of.

Create an introductory “scenario” of just two to three sentences that describes the data file for your project and why you (the ? Corporation/Group) are building a regression model to predict based on the set of possible independent variables.

To develop your project report ( to be submitted for grade in hard copy) follow the steps below.

Multiple regression in minitab express