MINITAB 15 BASICS

Dr. Nancy Pfenning
August 2007

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. If multiple worksheets are in use, you may acess other worksheets from the Window menu, upper right.

The menu bar across the top contains the main menus: File, Edit, Data, Calc, Stat, Graph, Editor, Tools, 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. Note that a height of ``5 foot 7" would be entered as 67, and ``6 foot 1" would be 73.

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 N, number of non-responses N*, mean, SE Mean, standard deviation, minimum, Q1, median, Q3, and maximum 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. Double-click on the Simple histogram (upper left)
  3. Specify FHts in the Graph variables text box
  4. Click OK

Example E:

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

Example F:

  1. Choose Graph>Boxplot...
  2. Double-click on the Simple boxplot, under One Y (upper left)
  3. Specify FHts in the Graph variables text box
  4. Click OK

To produce side-by-side boxplots of male and female heights,

  1. Choose Graph>Boxplot...
  2. Double-click on the Simple boxplot under Multiple Y's (lower left)
  3. Specify FHts and MHts in the Graph variables text box.
  4. Click OK

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

  1. Choose Data>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 Data>Sort
  4. Specify HTS in the sort columnn(s) text box, HTS in the By column box, and SORTEDHTS in the Store sorted data in: box, either in New worksheet or Column of current worksheet.
  5. Click OK

The remaining examples work with existing data that are to be downloaded into MINITAB. Data for dozens of variables about hundreds of students can be accessed on Dr. Pfenning's website http://www.pitt.edu/~nancyp/stat-0200/index.html where the file name is highlighted. To download into MINITAB, type ctrl A to highlight, ctrl C to copy, start up MINITAB, type ctrl V to paste it. If it asks about delimiters, click OK.

Example H Suppose all heights are entered in a single column HT, and genders (male or female) are entered in the column SEX. To compare heights of students in the two gender groups,

  1. Choose Stat>Basic Statistics>Display Descriptive Statistics...
  2. Specify HT in the Variables text box
  3. Specify SEX in the By variables text box
  4. Choose Graphs and check Boxplot of data
  5. Click OK
  6. Click OK

Now suppose all earnings are entered in a single column EARNED, and YEAR contains values 1, 2, 3, 4, and Other. To compare earnings of students in Years 1 to 4 only (if for some reason the Others are to be omitted),

  1. Choose Data>Unstack columns
  2. Specify EARNED for Unstack the data in and YEAR for Using subscripts in. By default, the unstacked columns EARNED_1 to EARNED_Other will be stored In a new worksheet, but you can also request After last column in use.
  3. Click OK
  4. Obtain desired descriptive statistics and displays for EARNED_1 to EARNED_4. [Boxplots would be Simple under Multiple Y's as in the second part of Example F.]
Example I Suppose all heights were entered in a single column HT, 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. Double-click on the With groups, One Y (upper right)
  3. Specify HT in the Graph variables text box and SEX in the Categorical variables for grouping text box.
  4. Click OK

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)" enter HT.
  3. After "Store samples in:" type the name of a new column, such as SampledHts. 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 Number of rows to sample
  8. Click in the From columns text box and specify NUMBERS
  9. Click in the Store samples in text box and type SampledNumbers
  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 Verbal SAT scores of surveyed students to be a random sample taken from scores of all Pitt students, whose mean score is unknown [actually, it is about 580] and standard deviation is assumed to be 111. Use sample scores to obtain a 90% confidence interval for population mean score.

  1. Choose Stat>Basic Statistics>1-Sample Z...
  2. Specify Verbal in the Samples in columns text box
  3. Click in the Standard deviation text box and type 111
  4. Select the Options button
  5. Click in the Confidence level text box and type 90
  6. Make sure Alternative is at the default not equal
  7. Click OK
  8. Click OK

Example M: Assume Verbal SAT scores of surveyed students members to be a random sample taken from scores of all Pitt students, whose mean and standard deviation are unknown. Use sample scores to obtain a 99% confidence interval for population mean score.

  1. Choose Stat>Basic Statistics>1-Sample t...
  2. Specify Verbal in the Samples in columns text box
  3. Select the Options button
  4. Click in the Confidence level text box and type 99
  5. Make sure Alternative is at the default not equal
  6. Click OK
  7. Click OK

STATISTICAL INFERENCE; HYPOTHESIS TESTS

Example N: Test the null hypothesis that Verbal SAT scores of surveyed students are a random sample taken from a population with mean 580 against the alternative that the mean is greater than 580. Assume population standard deviation to be 111. [If population standard deviation were not assumed to be known, a 1-Sample t test would be used, and Standard deviation would not be specified.]

  1. Choose Stat>Basic Statistics>1-Sample Z...
  2. Specify Verbal in the Samples in columns text box
  3. Click in the Standard deviation text box and type 111
  4. Check the Perform hypothesis test box and enter 580 in the hypothesized mean box
  5. Select the Options button
  6. Under Alternative select greater than
  7. Click OK
  8. Click OK

Example O: Do students' dads tend to be older than their moms? Test the null hypothesis that the mean of differences: (ages of dads minus ages of moms) for the larger population is zero vs. the alternative that the mean of differences is positive.

  1. Choose Stat>Basic Statistics>Paired t...
  2. Click in the First Sample text box and specify DadAge
  3. Click in the Second Sample text box and specify MomAge
  4. Click in the Options button
  5. Make sure the Test Mean text box says 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 one column option button and enter HT for Samples and SEX for subscripts...
  3. Click in the Options button
  4. Click the arrow button at the right of the Alternative drop-down list box and select less than
  5. 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.
  6. Click on Graphs and select Boxplots of data
  7. Click OK

Alternatively, the data may occur in two columns of height values, one for each sex.

  1. Select the Samples in different columns option button if that is the case
  2. Click in the First text box and specify FHTS
  3. Click in the Second text box and specify MHTS
  4. Proceed as above.

REGRESSION

Example Q: Use MINITAB to examine the relationship between ages of students fathers and ages of their mothers; after verifying the linearity of the scatterplot, find the correlation r and the regression equation; produce a fitted line plot. Produce a histogram of residuals and a plot of residuals vs. the explanatory variable (MomAge). Obtain a confidence interval for the mean height of all fathers when mothers are 40, and a prediction interval for an individual father when the mother is 40 years old.

  1. Choose Graph>Scatterplot and double-click on Simple
  2. Specify DadAge in the Y variables text box next to the 1
  3. Specify MomAge in the X variables text box next to the 1
  4. Click OK
  5. Choose Stat>Basic Statistics>Correlation...
  6. Specify MomAge and DadAge in the Variables text box
  7. Click OK.
  8. Choose Graph>Scatterplot and double-click on With regression line
  9. Specify DadAge in the Y variables text box next to the 1
  10. Specify MomAge in the X variables text box next to the 1
  11. Click OK.
  12. Choose Stat>Regression>Regression...
  13. Specify DadAge in the Response text box
  14. Click in the Predictors text box and specify MomAge
  15. Click on the Graphs... box
  16. Check the Histogram of residuals box
  17. In the Residuals versus the variables box, specify MomAge
  18. Click OK.
  19. Click OK.
  20. Choose Stat>Regression>Regression...
  21. Specify DadAge in the Response text box
  22. Specify MomAge in the Predictors text box
  23. Click in the Options...button
  24. Click in the Prediction intervals for new observations text box and type 40
  25. Click in the Confidence level text box and verify the default 95
  26. Click OK
  27. Click OK.

ANALYSIS OF VARIANCE (ANOVA)

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

  1. First unstack earnings according to year (see Example H).
  2. Choose Stat>ANOVA>Oneway (Unstacked)...
  3. Specify EARNED_1, EARNED_2, EARNED_3, EARNED_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 as it appears in the original worksheet by specifying EARNED in the Response box and YEAR as the Factor variable, using Stat>ANOVA>One Way.... In this case, the ``Other" students cannot be omitted.

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 Graph>Pie Chart and enter SEX as the Categorical variables
  2. Click OK.
  3. Choose Stat>Basic Statistics>1Proportion...
  4. Specify SEX for Samples in columns
  5. Click on Options to test a proportion other than the default, .5, or to specify a one-sided alternative.
  6. 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 Graph>Pie Chart and enter COLOR as the Categorical variables
  2. Click OK.
  3. Choose Stat>Tables>Tally Individual Variables
  4. Specify COLOR in the Variables box.
  5. Check Counts for Display box.
  6. Click OK.
  7. Note the count in the color of interest (events) and the total count N (trials).
  8. Choose Stat>Basic Statistics>1Proportion
  9. Activate the Summarized data button.
  10. Specify the numbers of trials and events.
  11. Click Perform hypothesis test to test a proportion other than the default, .5, or to specify a one-sided alternative. Click on Options and check "Use text and interval based on normal distribution" so your results will be consistent with our calculations by hand.
  12. 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 and Chi-Square
  2. Decide which should be the explanatory variable; in this case, it would be SEX. Specify SEX as the categorical variable for rows and YEAR for columns
  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 under the Chi-Square box
  5. Click OK.
  6. Click OK.
  7. Choose Graph>Bar Chart
  8. Double-click on Cluster
  9. Enter SEX and YEAR as the Categorical variables (SEX first because it is the explanatory variable, graphed horizontally)
  10. Click Chart Options.
  11. Select Show Y as Percent and Within Categories at level 1 to get side-by-side charts of percentages within each gender group.
  12. Click OK.
  13. Click OK.