MINITAB BASICS

Dr. Nancy Pfenning
February 2003

After starting MINITAB, you'll see a Session window above and a worksheet below. The Session window displays non-graphical output such as tables of statistics and character graphs. A worksheet is where we enter, name, view, and edit data. At any point, the session or worksheet window (whichever is currently active) may be printed by clicking on the print icon (third from left at top of screen) and clicking on OK.

The menu bar across the top contains the main menus: File, Edit, Manip, Calc, Stat, Graph, Editor, Window, and Help. Beneath the menu bar is the Toolbar which provides shortcuts for several important actions.

In the instructions that follow, text to be typed will be underlined. Menu instructions will be set in boldface type with the entries separated by pointers.

STORING DATA

Each data set is stored in a column, designated by a "C" followed by a number. For example, C1 stands for Column 1. The column designations are displayed along the top of the worksheet. The numbers at the left of the worksheet represent positions within a column and are referred to as rows. Each rectangle occurring at the intersection of a column and a row is called a cell. It can hold one observation.

The active cell has the worksheet cursor inside it and a dark rectangle around it. To enter or change an observation in a cell, we first make the cell active and then type the value.

Directly below each column label in the worksheet is a cell optionally used for naming the column. To name the column, we click on this cell and type the desired name.

Example A: Suppose we want to store heights, in inches, of female recitation members [64, 65, 61, 70, 65, 66, ...] into column C1 and name the column ‘FHTS'. Just click in the name cell for this column, type FHTS, and press the "Enter" key. Then type 64, Enter, 65, Enter, 61, Enter, and so on.

Example B: To store male heights, name column C2 "MHTS" and enter those data values in this column.

DESCRIPTIVE STATISTICS AND GRAPHS

Example C: For sample size, mean, median, 5% trimmed mean, standard deviation, minimum, maximum, and quartiles of female height data,

  1. Choose Stat>Basic Statistics>Display Descriptive Statistics...
  2. Specify FHTS in the Variables text box (instead of typing it directly, you may double-click on FHTS in the box on the left)
  3. Click OK

For histogram(D), stemplot(E), and boxplot(F) of female height data,

Example D:

  1. Choose Graph>Histogram...
  2. Specify FHTS in the X text box for Graph 1
  3. Click OK

Example E:

  1. Choose Graph>Stem-and-Leaf...
  2. Specify FHTS in the Variables text box
  3. Click OK

Example F:

  1. Choose Graph>Boxplot...
  2. Specify FHTS in the Y text box for Graph 1
  3. Click OK

Example G: To combine and sort female and male recitation members' heights,

  1. Choose Manip>Stack>Stack Columns
  2. Specify FHTS and MHTS with a space between them as columns to be stacked. Click the Column of current worksheet button and type HTS in this box (Click OK)
  3. Choose Manip>Sort
  4. Specify HTS as column to be sorted, specify SORTEDHTS in the Store sorted columns in: box, and HTS in the Sort by column box
  5. Click OK

Example H Suppose all heights were entered in a single column HTS, and genders (M or F) were entered in the column SEX. To compare heights of students in the two gender groups,

  1. Choose Manip>Unstack
  2. Specify HTS for "Unstack the data in" and SEX for "Using subscripts in". By default, the unstacked columns HTS_M and HTS_F will be stored in a new worksheet.
  3. Click OK
  4. Obtain desired descriptive statistics and displays for the various groups.
Example I Suppose all heights were entered in a single column HTS, and genders (M or F) were entered in the column SEX. To produce side-by-side boxplots of male and female heights,
  1. Choose Graph>Boxplot
  2. Specify HTS as the measurement variable Y and SEX as the categorical variable X.
  3. Click OK

Now suppose heights of males and females have been unstacked into two columns HTS_M and HTS_F. To produce side-by-side boxplots of male and female heights,

  1. Choose Stat>Basic Statistics>2-Sample t.
  2. Select Samples in different columns. Specify HTS_M as the First column and HTS_F as the Second column.
  3. Click on the Graphs... box.
  4. Check the box for boxplots of data.
  5. Click OK
  6. Click OK

For more than two side-by-side boxplots when the data are unstacked, see the ANOVA example.

RANDOM SAMPLING

Example J We can use MINITAB to take a random sample of, say, 10 heights from those in a data column.

  1. Choose Calc>Random Data>Sample From Columns
  2. Type 10 in the box to specify how many rows, and after "from column(s)" type HTS.
  3. After "Store samples in:" type the name of an empty column, such as C25. Do not check the "sample with replacement" box.
  4. Click OK

Note: for independent samples (such as for two-sample t or ANOVA), perform the above steps twice. To sample pairs of values (such as for paired t or regression), two columns of equal length can be specified (eg. MOMAGE and DADAGE) and then two empty columns must be specified for storage.

Example K: We can also use MINITAB to randomly select 5 from 100 names in a hard-copy list. Assume the names are listed alphabetically, where the first name corresponds to the number 1 and the last corresponds to the number 100.

  1. Choose Calc>Make Patterned Data>Simple Set of Numbers...
  2. Type NUMBERS in the Store Patterned Data text box
  3. Click in the From first value text box and type 1
  4. Click in the To last value text box and type 100
  5. Click OK
  6. Choose Calc>Random Data>Sample From Columns...
  7. Type 5 in the small text box after Sample
  8. Click in the Sample...rows from columns text box and specify NUMBERS
  9. Click in the Store sample in text box and type SAMPLE
  10. Click OK

STATISTICAL INFERENCE; CONFIDENCE INTERVALS

Note: Confidence intervals are automatically provided in the output for a hypothesis test, but it will not be the standard confidence interval unless the two-sided alternative has been selected.

Example L: Assume heights (in inches) of female recitation members to be a random sample taken from heights of all female college students, whose mean height is unknown [actually, it is about 65] and standard deviation is 2.5. Use sample heights to obtain a 90% confidence interval for population mean height.

  1. Choose Stat>Basic Statistics>1-Sample Z...
  2. Specify FHTS in the Variables text box
  3. Select the Confidence interval option button
  4. Click in the Level text box and type 90
  5. Click in the Sigma text box and type 2.5
  6. Click OK

Example M: Assume heights (in inches) of male recitation members to be a random sample taken from heights of all male college students, whose mean and standard deviation are unknown. Use sample heights to obtain a 99% confidence interval for population mean height.

  1. Choose Stat>Basic Statistics>1-Sample t...
  2. Specify MHTS in the Variables text box
  3. Select the Confidence interval option button
  4. Click in the Level text box and type 99
  5. Click OK

STATISTICAL INFERENCE; HYPOTHESIS TESTS

Example N: Test the null hypothesis that heights (in inches) of female recitation members are a random sample taken from a population with mean 65 against the alternative that the mean is different from 65. Assume population standard deviation to be 2.5. [If population standard deviation were not assumed to be known, a 1-Sample t test would be used, and Sigma would not be specified.]

  1. Choose Stat>Basic Statistics>1-Sample Z...
  2. Specify FHTS in the Variables text box
  3. Select the Test mean option button
  4. Click in the Test mean text box and type 65
  5. Click the arrow button at the right of the Alternative drop-down list box and select not equal to
  6. Click in the Sigma text box and type 2.5
  7. Click OK

Example O: Do sons tend to be taller than their fathers? Test the null hypothesis that the difference: (heights of male recitation members minus heights of their fathers) is zero vs. the alternative that the difference is positive. First enter male recitation members' heights in a column ‘SONS' and their corresponding fathers' heights in a column ‘FATHERS'.

  1. Choose Stat>Basic Statistics>Paired t...
  2. Click in the First Sample text box and specify SONS
  3. Click in the Second Sample text box and specify FATHERS
  4. Click in the Options button
  5. Click in the Test Mean text box and type 0
  6. Click the arrow button at the right of the Alternative drop-down list box and select greater than
  7. Click OK
  8. Click OK

Example P: Use MINITAB to verify that female heights are significantly less than male heights. Procedure may or may not be pooled.

  1. Choose Stat>Basic Statistics>2-Sample t...
  2. Select the Samples in different columns option button if that is the case
  3. Click in the First text box and specify FHTS
  4. Click in the Second text box and specify MHTS
  5. Click the arrow button at the right of the Alternative drop-down list box and select less than
  6. If sample standard deviations are close and you have reason to assume equal population variances, you may select the Assume equal variances check box, which carries out a pooled procedure. Otherwise, unselect it.
  7. Click OK
  8. Select the Samples in one column option button if that is the case, enter HTS for Samples and SEX for subscripts...

REGRESSION

Example Q: Use MINITAB to examine the relationship between heights of male recitation members and heights of their fathers; after verifying the linearity of the scatterplot, find the correlation r and the regression equation; produce a fitted line plot. Produce a list of residual, a histogram of residuals and a plot of residuals vs. the explanatory variable (FATHERS). Obtain a confidence interval for the mean height of all sons of 70-inch-tall fathers and a prediction interval for an individual son of a 70-inch-tall father.

  1. Choose Graph>Plot
  2. Specify SONS in the Y text box for Graph 1
  3. Click in the X text box for Graph 1 and specify FATHERS
  4. Click OK
  5. Choose Stat>Basic Statistics>Correlation...
  6. Specify FATHERS and SONS in the Variables text box
  7. Click OK.
  8. Choose Stat>Regression>Fitted Line Plot...
  9. Specify SONS in the Response text box
  10. Click in the Predictors text box and specify FATHERS
  11. Click OK.
  12. Choose Stat>Regression>Regression...
  13. Specify SONS in the Response text box
  14. Click in the Predictors text box and specify FATHERS
  15. Click on the Results... box
  16. Select In addition, the full table of fits and residuals
  17. Click OK.
  18. Click on the Graphs... box
  19. Check the Histogram of residuals box
  20. In the Residuals versus the variables box, specify FATHERS
  21. Click OK.
  22. Click OK.
  23. Choose Stat>Regression>Regression...
  24. Specify SONS in the Response text box
  25. Click in the Predictors text box and specify FATHERS
  26. Click in the Options...button
  27. Click in the Prediction intervals for new observations text box and type 70
  28. Click in the Confidence level text box and type 95
  29. Click OK
  30. Click OK.

ANALYSIS OF VARIANCE (ANOVA)

Example R: Use MINITAB to see if there is a significant difference in mean heights of freshmen, sophomores, juniors, and seniors in the class. Include side-by-side boxplots to display the data.

  1. First unstack heights according to year (see Example H).
  2. Choose Stat>ANOVA>Oneway (Unstacked)...
  3. Specify HTS_1, HTS_2, HTS_3, HTS_4 in the Responses text box.
  4. Click on the Graphs... box
  5. Check the box for Boxplots of data
  6. Click OK.
  7. Click OK.

You may also compare mean responses of stacked data by specifying HTS in the Response box and YEAR as the Factor variable, using Stat>ANOVA>Oneway....

SINGLE PROPORTIONS

Example S: Use MINITAB to do inference about the population proportion of males/females. [The following only works for categorical variables like SEX that have just 2 possibilities.]

  1. Choose Stat>Basic Statistics>1Proportion...
  2. Specify SEX for Samples in columns
  3. Click on Options to test a proportion other than the default, .5, or to specify a one-sided alternative.
  4. Click OK.

Example T: Use MINITAB to do inference about the population proportion preferring a certain color. These steps may be followed if the variable of interest has more than 2 possibilities.

  1. Choose Stat>Tables>Tally
  2. Specify COLOR in the Variables box.
  3. Click OK.
  4. Note the count in the color of interest (successes) and the total count N (trials).
  5. Choose Stat>Basic Statistics>1Proportion
  6. Activate the Summarized data button.
  7. Specify the numbers of trials and successes.
  8. Click on Options to test a proportion other than the default, .5, or to specify a one-sided alternative. Also, check "Use text and interval based on normal distribution" so your results will be consistent with our calculations by hand.
  9. Click OK.

TWO-WAY TABLES and CHI-SQUARE

Example U: Use MINITAB to check for a relationship between gender and year at Pitt.

  1. Choose Stat>Tables>Cross Tabulation
  2. Decide which should be the explanatory variable; in this case, it would be SEX. Specify SEX and YEAR as the classification variables (the explanatory variable should go first).
  3. For data analysis, check Counts and Row percents under Display. The row percents are conditional percentages for respective values of the explanatory variable.
  4. For statistical inference, check the Chi-Square analysis box.
  5. Click OK.