Dr. Nancy Pfenning
April 2002
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.
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.
Example C: For sample size, mean, median, 5% trimmed mean, standard deviation, minimum, maximum, and quartiles of female height data,
For histogram(D), stemplot(E), and boxplot(F) of female height data,
Example D:
Example E:
Example F:
Example G: To combine and sort female and male recitation members' heights,
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,
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,
For more than two side-by-side boxplots when the data are unstacked, see the ANOVA example.
Example J We can use MINITAB to take a random sample of, say, 10 heights from those in a data column.
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.
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.
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.
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.]
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'.
Example P: Use MINITAB to verify that female heights are significantly less than male heights. Procedure may or may not be pooled.
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.
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.
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....
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.]
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.
Example U: Use MINITAB to check for a relationship between gender and year at Pitt.