1. Chapter 9 Minitab Recipe Cards

2. Contingency tests Enter the data from Example 9.1 in C1, C2 and C3.

3. Select Tables from the Stat menu and choose Chi-Square Test (Two-Way table in Worksheet) from the sub-menu.

4. Type C2 and C3 as the Columns containing the table and click OK.

5. The output includes the test statistic, Chi- Sq, the degrees of freedom, DF and the probability of getting the test statistic if there were no association, the P- Value.

6. Hypothesis tests for regression model coefficients Enter the data from Example 9.6 into two worksheet columns.

7. Choose Regression from the Stat menu and Regression from the sub-menu.

8. Type C2 in the Response box and C1 in the Predictor box. Click OK.

9. The analysis in the session window an analysis begins with the regression equation. In the table below it the Predictor column lists the two components of the model; the Constant (intercept) and the temperature variable. The Coef (coefficient) column contains the sample intercept and the sample slope.

10. The SECoeff column contains the estimated standard errors of the sample intercept and slope. The T column contains the test statistics based on the sample intercept (the upper figure) and the sample slope (the lower figure). The adjacent P column contains the p-values for the sample intercept and the sample slope.

11. The figures in the P column assess the hypotheses that the population intercept and slope are zero. The first is the probability that the sample intercept is 0.738 or more if the population intercept is zero. The P value of 0.917 suggests that it is zero. The second P value, 0.001, is the probability that the sample slope is 2.3788 or more if the population slope is zero. The figure is below the level of significance, 0.05 the null hypothesis should be rejected. S is the standard deviation of the residuals.

12. Residual plots Enter the data from Example 9.6 into two worksheet columns.

13. Select Regression from Stat menu and Regression from the sub-menu.

14. Type C2 and C1as the Response and Predictor variables respectively then click the Graphs button.

15. Click next to Residuals versus fits then click OK and OK on the Regression command window.

16. The graph that appears is the plot of the residuals against the fits (the values of the Y variable that should, according to the line, have occurred). This shows whether there is some systematic variation not explained by the model.