You will need Minitab for this project. I will also post the excel file that you will need for this project below. All the questions should have a good narrative and pictures from the Minitab.

I will send you an excel file. The data can be copied to Minitab as it is and then you cal solve it through Minitab.All the stepwise instructions are below. I can also send you the lectures if you need more help.

1. Incorporate seasonal dummies and trends into your model. Identify if you have seasonality, trend by checking their significance? Is that consistent with your previous findings?

2. Check all your explanatory variables check for multicollinearity again (scatter plots, VIF, correlations). If you have a sign switch, correct the situation by throwing one (and if necessary more) of the variables out of the model. Consider R-squared or adj R-squared when making the decision. Your model should ONLY have variables with a correct sign that have the highest combined adj-R-squared.

3. Using the scatter plots you generated, identify any nonlinear relationships between Y and X variables.

Try to correct nonlinearity through transformation (page 233-237). If it works, keep the transformed version of the variable. Otherwise, use the original variable, acknowledge the nonlinearity and move on to the next test. Use 2 different transformations (ex: Log X, 1/X, X^2 or SQRT(X)).

4. Once you correct for nonlinearity and multicollinearity, check for autocorrelation using the DW test. Do you have autocorrelation? Correct for autocorrelation if you have any. (HINT: You may have to check for sign switch again)

5. Once you corrected for all possible problems, analyze the resulting residuals (4-in-11 plot in MINITAB)

6. Once you have your final model, use the forecasted X values (in PP2) and forecast your Y variable for 10 periods. Beware: if you transformed any variables, you may have to adjust your forecasts.