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Chapter 4 SPSS: Summarising bivariate data

Chapter 4 SPSS: Summarising bivariate data. Enter the data from Example 4.1 into two columns then c lick Variable View . Name the variables Price and Sales. Change the Measure to Scale for both variables. Correlation coefficient.

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Chapter 4 SPSS: Summarising bivariate data

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  1. Chapter 4SPSS: Summarising bivariate data

  2. Enter the data from Example 4.1 into two columns then click Variable View. Name the variables Price and Sales. Change the Measure to Scale for both variables. Correlation coefficient

  3. Choose Correlate from the Analyze menu then select Bivariate from the sub-menu.

  4. In the new window click Price then click the right arrow to the left of the space under Variables. Repeat the process for Sales. Click OK.

  5. The coefficient appears in the Output Viewer.

  6. Put the data from Example 4.1 into two worksheet columns and configure the variables as outlined on slide 2. Simple linear regression

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

  8. In the command window click Sales then click the right arrow to the left of the space under Dependent. Click Price then click the right arrow to the left of the space under Independent(s). Click OK.

  9. The Coefficients table in the Output Viewerhas the intercept and slope of the line of best-fit in the B column under Unstandardized Coefficients.

  10. The Seasonal Decomposition facility in SPSS requires four data cycles so enter the sales from Example 4.15 into a worksheet column in chronological order and add the following eight values: 16.4, 38.3, 37.9, 7.5, 17.2, 40.0, 40.5, 8.2. Time series decomposition

  11. Select Define date and time from the Data menu.

  12. Choose Years, quarters under Cases Are. Below First Case Is: make the Year 1 and the Quarter 1, and check that the Periodicity at higher level is 4.

  13. Click OK and columns of data headed YEAR, QUARTER and DATE appear in the worksheet.

  14. Select Forecasting from the Analyze menu and Seasonal Decomposition from the sub-menu.

  15. In the command window move Sales variable to the space below Variable(s) and select Additive under Model. Click OK.

  16. Four new columns of data appear in the worksheet; ERR (the errors), SAS (the seasonally adjusted series), SAF (seasonal adjustment factors), and STC (smoothed trend cycle).

  17. For a plot of the series with the trend estimates select Forecasting from the Analyze menu and Sequence Charts from the sub-menu.

  18. Move Sales and Trend-cycle to the space below Variables and Date.format to the space below Time Axis Labels. Click OK.

  19. The graph appears in the Output Viewer.

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