Minitab instructions

These instructions accompany Applied Regression Modeling by Iain Pardoe, 2nd edition published by Wiley in 2012. The numbered items cross-reference with the "computer help" references in the book. These instructions are based on Minitab 17 for Windows, but they (or something similar) should also work for other versions. Find instructions for other statistical software packages here.

Getting started and summarizing univariate data

  1. If desired, change Mintab's default options by selecting Tools > Options.
  2. To open a Mintab data file, select File > Open.
  3. To edit last dialog box, select Edit > Edit Last Dialog or click the Edit Last Dialog tool (ninth button from the left).
  4. Output appears in the Session Window and can be copied and pasted from Minitab to a word processor like OpenOffice Writer or Microsoft Word. Graphs appear in separate windows and can also easily be copied and pasted to other applications.
  5. You can access help by selecting Help > Help. For example, to find out about "boxplots" click the Index tab, type boxplots in the first box, and select the index entry you want in the second box.
  6. To transform data or compute a new variable, select Calc > Calculator. Type a name (with no spaces) for the new variable in the Store result in variable box, and type a mathematical expression for the variable in the Expression box. Current variables in the dataset can be moved into the Expression box, while the keypad and list of functions can be used to create the expression. Examples are LOGE('X') for the natural logarithm of X and 'X'**2 for X2. Click OK to create the new variable, which will be added to the dataset (check it looks correct in the Worksheet Window); it can now be used just like any other variable. If you get the error message "Completion of computation impossible," this means there is a syntax error in your Expression—a common mistake is to forget the multiplication symbol (*) between a number and a variable (e.g., 2*'X' represents 2X).
  7. To create indicator (dummy) variables from a qualitative variable, select Calc > Make Indicator Variables. Move the qualitative variable into the Indicator variables for box, type a range of columns in which to store the variables (e.g., C5-C6) in the Store results in box, and click OK (check that the correct indicator variables have been added to your spreadsheet in the Worksheet Window).
  8. Calculate descriptive statistics for quantitative variables by selecting Stat > Basic Statistics > Display Descriptive Statistics. Move the variable(s) into the Variable(s) list. Click Statistics to select the summaries, such as the Mean, that you would like.
  9. Create contingency tables or cross-tabulations for qualitative variables by selecting Stat > Tables > Cross Tabulation and Chi-Square. Move one qualitative variable into the rows box and another into the columns box. Cell percentages (within rows, columns, or the whole table) can be calculated by clicking the appropriate boxes under Display.
  10. If you have a quantitative variable and a qualitative variable, you can calculate descriptive statistics for cases grouped in different categories by selecting Stat > Tables > Descriptive Statistics. Move the qualitative variable into the rows box (and another qualitative variable into the columns box if there is more than one). Click Associated Variables to select the quantitative variable for which you would like descriptive statistics, and the descriptive statistics to display; the default is the number of cases, but other statistics such as the Mean and Standard Deviation can also be selected.
  11. To make a stem-and-leaf plot for a quantitative variable, select Graph > Stem-and-Leaf. Move the variable into the Graph variables box.
  12. To make a histogram for a quantitative variable, select Graph > Histogram. Choose Simple and move the variable into the Graph variables box.
  13. To make a scatterplot with two quantitative variables, select Graph > Scatterplot. Choose Simple and move the vertical axis variable into the first row of the Y variables column and the horizontal axis variable into the first row of the X variables column.
  14. All possible scatterplots for more than two variables can be drawn simultaneously (called a scatterplot matrix) by selecting Graph > Matrix Plot, choosing Matrix of plots, Simple, and moving the variables into the Graph variables list.
  15. You can mark or label cases in a scatterplot with different colors/symbols according to categories in a qualitative variable by selecting Graph > Scatterplot and choosing With Groups. After moving the vertical axis variable into the first row of the Y variables column and the horizontal axis variable into the first row of the X variables column, move the grouping variable into the Categorical variables for grouping box. To change the colors/symbols used, select the symbols you want to change by clicking on one of the points with that symbol twice (all the data points should become highlighted on the first click, and just the points in that group should remain highlighted on the second click). Then select Editor > Edit Symbols. Select the color/symbol you want and click OK to see the effect.
  16. You can identify individual cases in a scatterplot by hovering over them.
  17. To remove one of more observations from a dataset, select Data > Subset Worksheet. Select Specify which rows to exclude and select one of the subsequent options.
  18. To make a bar chart for cases in different categories, select Graph > Bar Chart.
  19. To make boxplots for cases in different categories, select Graph > Boxplot. Choose One Y, With Groups, move the quantitative variable into the Graph variables box, and move the qualitative variable(s) into the Categorical variables box.
  20. To make a QQ-plot (also known as a normal probability plot) for a quantitative variable, select Graph > Probability Plot. Choose Single and move the variable into the Graph variables box.
  21. To compute a confidence interval for a univariate population mean, select Stat > Basic Statistics > 1-Sample t. Move the variable for which you want to calculate the confidence interval into the Samples in columns box. Then click the Options button to bring up another dialog box in which you can specify the confidence level for the interval. Clicking OK will take you back to the previous dialog box, where you can now click OK.
  22. To do a hypothesis test for a univariate population mean, select Stat > Basic Statistics > 1-Sample t. Move the variable for which you want to do the test into the Samples in columns box, check Perform hypothesis test, and type the (null) hypothesized value into the Hypothesized mean box. Then click the Options button to bring up another dialog box in which you can specify a lower-tailed ("less than"), upper-tailed ("greater than"), or two-tailed ("not equal") alternative hypothesis. OK will take you back to the previous dialog box, where you can now click OK.

Simple linear regression

  1. To fit a simple linear regression model (i.e., find a least squares line), select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variable into the Predictors box. Just click OK for now—the other items in the dialog box are addressed below. In the rare circumstance that you wish to fit a model without an intercept term (regression through the origin), click the Model button and deselect Include the constant term in the model before clicking OK.
  2. To add a regression line or least squares line to a scatterplot, select Editor > Add > Regression Fit, and Linear for the Model Order. You can create a scatterplot with a regression line superimposed by selecting Graph > Scatterplot. Choose With Regression and move the response variable into the first row of the Y variables column and the predictor variable into the first row of the X variables column.
  3. To find 95% confidence intervals for the regression parameters in a linear regression model, select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variable into the Predictors box. Before clicking OK, click the Results button, select Expanded Table and check Coefficients. The confidence intervals are displayed as the final two columns of the "Coefficients" output. This applies more generally to multiple linear regression also.

Multiple linear regression

  1. To fit a multiple linear regression model, select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variables into the Predictors box. In the rare circumstance that you wish to fit a model without an intercept term (regression through the origin), click the Options button and deselect Fit intercept before clicking OK.
  2. To add a quadratic regression line to a scatterplot, select Editor > Add > Regression Fit, and Quadratic for the Model Order. You can create a scatterplot with a quadratic regression line superimposed by selecting Graph > Scatterplot. Choose With Regression and move the vertical axis variable into the first row of the Y variables column and the horizontal axis variable into the first row of the X variables column. Before clicking OK, click the Data View button, click the Regression tab in the subsequent Scatterplot - Data View dialog box, and change the Model Order from Linear to Quadratic. Click OK to return to the Scatterplot - With Regression dialog box, and OK again to create the graph.
  3. Categories of a qualitative variable can be thought of as defining subsets of the sample. If there is also a quantitative response and a quantitative predictor variable in the dataset, a regression model can be fit to the data to represent separate regression lines for each subset. To display a regression line for each subset in a scatterplot, select Graph > Scatterplot and choose With Regression and Groups. After moving the vertical axis variable into the first row of the Y variables column and the horizontal axis variable into the first row of the X variables column, move the grouping variable into the Categorical variables for grouping box. Click OK to create the graph.
  4. Minitab does not appear to offer an automatic way to find the F-statistic and associated p-value for a nested model F-test in multiple linear regression. It is possible to calculate these quantities by hand using Minitab regression output and appropriate percentiles from a F-distribution.
  5. To save residuals in a multiple linear regression model, select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variables into the Predictors box. Before clicking OK, click the Storage button and check Residuals under Diagnostic Measures in the subsequent Regression: Storage dialog box. Click OK to return to the main Regression dialog box, and then click OK. The residuals are saved as a variable called RESI in the Worksheet Window; they can now be used just like any other variable, for example, to construct residual plots. Each time you ask Minitab to save residuals like this, it will add a new variable to the dataset and increment an end digit by one; for example, the second time you save residuals they will be called RESI_1. To save what Pardoe (2012) calls standardized residuals, check Standardized residuals under Diagnostic Measures in the Regression: Storage dialog box—they will be saved as a variable called SRES in the Data Editor Window. To save what Pardoe (2012) calls studentized residuals, check Deleted t residuals under Diagnostic Measures in the Regression: Storage dialog box—they will be saved as a variable called TRES in the Data Editor Window.
  6. To add a loess fitted line to a scatterplot (useful for checking the zero mean regression assumption in a residual plot), select Editor > Add > Smoother. The default value of 0.5 for Degree of smoothing tends to be a little on the low side: I would change it to 0.75. You can create a scatterplot with a loess fitted line superimposed by selecting Graph > Scatterplot. Choose With Regression and move the vertical axis variable into the first row of the Y variables column and the horizontal axis variable into the first row of the X variables column. Before hitting OK, click the Data View button, click the Smoother tab in the subsequent Scatterplot - Data View dialog box, and change the Smoother from None to Lowess. Hit OK to return to the Scatterplot - With Regression dialog box, and OK again to create the graph.
  7. To save leverages in a multiple linear regression model, select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variables into the Predictors box. Before clicking OK, click the Storage button and check Hi (leverages) under Diagnostic Measures in the subsequent Regression: Storage dialog box. Click OK to return to the main Regression dialog box, and then hit OK. The leverages are saved as a variable called HI1 in the Worksheet Window; they can now be used just like any other variable, for example, to construct scatterplots. Each time you ask Minitab to save leverages like this, it will add a new variable to the dataset and increment an end digit by one; for example, the second time you save leverages they will be called HI_1.
  8. To save Cook's distances in a multiple linear regression model, select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variables into the Predictors box. Before clicking OK, click the Storage button and check Cook's distance under Diagnostic Measures in the subsequent Regression: Storage dialog box. Click OK to return to the main Regression dialog box, and then hit OK. Cook's distances are saved as a variable called COOK in the Worksheet Window; they can now be used just like any other variable, for example, to construct scatterplots. Each time you ask Minitab to save Cook's distances like this, it will add a new variable to the dataset and increment an end digit by one; for example, the second time you save Cooks' distances they will be called COOK_1.
  9. To create some residual plots automatically in a multiple linear regression model, select Stat > Regression > Regression > Fit Regression Model. Move the response variable into the Response box and the predictor variables into the Predictors box. Before clicking OK, click the Graphs button and select Deleted under Residuals for Plots in the subsequent Regression - Graphs dialog box. Check Residuals versus fits under Individual plots to create a scatterplot of the studentized residuals on the vertical axis versus the predicted values on the horizontal axis. You could also move individual predictor variables into the Residuals versus the variables box to create residual plots with each predictor variable on the horizontal axis. Click OK to return to the main Regression dialog box, and then hit OK. To create residual plots manually, first create studentized residuals (see help #35), and then construct scatterplots with these studentized residuals on the vertical axis.
  10. To create a correlation matrix of quantitative variables (useful for checking potential multicollinearity problems), select Stat > Basic Statistics > Correlation. Move the variables into the Variables box and hit OK.
  11. Minitab now displays variance inflation factors by default in multiple linear regression. The variance inflation factors are in the last column of the main regression output under "VIF."
  12. To draw a predictor effect plot for graphically displaying the effects of transformed quantitative predictors and/or interactions between quantitative and qualitative predictors in multiple linear regression, first create a variable representing the effect, say, "X1effect" (see computer help #6). Then select Graph > Scatterplot. Choose With Connect and Groups and move the "X1effect" variable into the first row of the Y variables column and X1 into the first row of the X variables column. See Section 5.5 in Pardoe (2012) for an example.

Last updated: Oct 2016

© 2016, Iain Pardoe