1. Incorporate seasonal dummies and trend 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 formulticollinearity again (scatter plots, VIF, correlations). If you have 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. You model should ONLY have variables with correct sign that have the higest 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 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.