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Bivariate Data Analysis Methods in Minitab

Learn how to summarize bivariate data, calculate correlation coefficients, and perform linear regression in Minitab using Example 4.1. Explore time series decomposition with graphical displays for trend estimates.

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Bivariate Data Analysis Methods in Minitab

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  1. Chapter 4Minitab: Summarising bivariate data

  2. Enter the data from Example 4.1 in columns C1 and C2 of the worksheet. Correlation coefficient

  3. Select Basic Statistics from the Stat menu and Correlation from the sub-menu.

  4. Type C1 and C2 in the space under Variables. Click OK.

  5. The correlation coefficient appears in the session window.

  6. Put the data from Example 4.1 in two columns of the worksheet. Simple linear regression

  7. Select Regression from the Stat menu then Regression from the Regression sub-menu then Fit Regression Model from the next menu.

  8. Type C2 in the space below Responses and C1 in the space below ContinuousPredictors.

  9. Click OK and the output that appears has the regression equation, the equation of the line of best fit, at the bottom.

  10. For a scatter diagram with the line of best-fit superimposed on the scatter like Figure 4.11 follow the Stat – Regression sequence and choose Fitted Line Plot from the Regression sub-menu.

  11. Type C2 beside Response (Y) and C1 beside Predictor (X). Click OK.

  12. The diagram that appears includes the regression equation and the value of R2 for the data.

  13. Put the data from Example 4.12 into C1 in chronological order. Time series decomposition

  14. Select Time Series from the Stat menu then Decomposition from the sub-menu.

  15. Type C1 to the right of Variable,type 3 as the Seasonal length and select the Additive model under Model Type. Click OK.

  16. The output generated includes three graphs, the third of which is a plot of the series with the trend estimates.

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