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Statistical Decision Making with Bivariate Data in SPSS

Learn how to analyze bivariate data and make statistical decisions using SPSS. Topics include contingency tests, hypothesis testing for regression model coefficients, and residual plots.

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Statistical Decision Making with Bivariate Data in SPSS

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  1. Chapter 9SPSS: Statistical decision making with bivariate data

  2. Enter the Example 9.1 data in the worksheet; the row numbers in the first column, the column numbers in the second and the observed values in the third. Click Variable View and name these columns Row, Column, and Observed. Contingency tests

  3. Choose Weight Cases from the Data menu.

  4. In the next window click Weight cases by. Migrate Observed to the space under Frequency Variable. Click OK.

  5. Choose Descriptive Statistics from the Analyze menu and select Crosstabs from the sub-menu.

  6. Move Row to the space under Row(s) and Column to the space under Column(s). Click the Statistics button.

  7. In the Crosstabs: Statistics window click the space to the left of Chi-square then click the Continue button. In the Crosstabs window Click the Cells button.

  8. In the Crosstabs: Cell Display windowclick the spaces to the left of Observed and Expected in the Counts section to the top left of the window. Click the Continue button then OK in the Crosstabs window.

  9. The Row * ColumnCrosstabulation in the Output Viewer is the contingency table. Below, the Chi-Square Tests includes the Value of the Pearson Chi-Square test statistic, df(degrees of freedom), and Asymptotic Significance (2-sided), the probability of the test statistic, or a higher one occurring.

  10. Enter the data from Example 9.6 into two worksheet columns. Click Variable View and name these columns Temperature and Sales. Hypothesis test for regression model coefficients

  11. Choose Regression from the Analyze menu and Linear from the sub-menu.

  12. Make Sales the Dependent variable and Temperature the Independent. Click OK.

  13. The Coefficients table has: The intercept (Constant) and slope in column B. The estimated standard errors of the sample intercept, (Constant) and slope, Temperature,under Std. Error. The test statistics based on the sample intercept and slope under t. The significance values for the sample intercept and slope under Sig.

  14. Put the data from Example 9.6 into two worksheet columns. Click Variable View and name these columns Temperature and Sales. Residuals plot

  15. Choose Regression from the Analyze menu and Linear from the sub-menu.

  16. Make Sales the Dependent variable and Temperature the Independent variable. Click the Plots button.

  17. Tick both options under Standardized Residual Plots then click Continue and OK in the command window.

  18. The diagrams in the Output Viewer show residuals from the data plotted against reference lines. Departures from these lines suggest some of the systematic variation in the data is not explained by the model.

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