Dr. Nancy Pfenning
After starting MINITAB, you'll see a Session window above and a worksheet below. The Session window displays both graphs and non-graphical output such as tables of statistics and character graphs. A worksheet is where we enter, name, view, and edit data. The Navigator bar at the left enables you to access any of the summaries or graphs produced during your session. You can highlight a specific item and right-click to Delete or Export as PDF or HTML.
The menu bar across the top contains the main menus: File, Edit, Data, Graph, Statistics, View, Window, and Help. Beneath each item in the menu bar is a drop-down list of 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. Variable names begin with a capital letter.
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 blue 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.
Whenever a variable name is to be entered in a text box, instead of typing it directly, you may double-click on its name in the box on the left.
Example A: Suppose we want to store heights, in inches, of female class 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.
Note: It is possible to opt out of unwanted summaries by choosing Statistics from the middle of the upper box under Descriptive Statistics, and unchecking them.
Example C: For sample size N, number of non-responses N*, Mean, SE Mean, StDev, Minimum, Q1, Median, Q3, and Maximum of female height data,
For histogram(D), stemplot(E), and boxplot(F) of female height data,
To produce side-by-side boxplots of male and female heights,
Example G: To combine and sort female and male class members' heights,
In Example G, you can also opt to "Store the sorted data in the original columns." Unlike its predecessors, Minitab for the Mac doesn't give the user additional options for where the stacked data should be stored, such as into a new worksheet, or into a new column specified with a new column name. To store in a new worksheet, simply cut the column, open a new worksheet, and paste it in. The column can be given a more meaningful name by accessing and changing it directly in the worksheet.
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 and ctrl C to copy. Start up MINITAB [or if it's already running, choose File>New to open up a new worksheet] , type ctrl V to paste it. Important: When you paste the data, have the cursor on the blank shaded cell under C1 but above Row 1. This puts the column names where they belong, so they will not be treated as data values.
Example H Suppose all heights are entered in a single column Height, and genders (male or female) are entered in the column Gender. To compare heights of students in the two gender groups,
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),
Observation: in Example H, the new columns are automatically called Earned_1 through Earned_Other and are stored in additional columns at the end of the same worksheet. Unlike its predecessors, Minitab for the Mac doesn't give users the option of storing the data in a new worksheet.Example I Suppose all heights were entered in a single column Height, and genders (M or F) were entered in the column Gender. To produce side-by-side boxplots of male and female heights,
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).
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.
I see you took my suggestion to replace One-Sample Hypothesis Tests with 1-Sample Inference but maybe my other suggestion 1-Sample Tests or Intervals is better? Same goes for 2-Sample Tests or Intervals.
Minor observation: why is it capital Z-test and lower-case t-test?
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 625] and standard deviation is assumed to be 100. Use sample scores to obtain a 90% confidence interval for population mean score.
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.
Example N: Test the null hypothesis that Verbal SAT scores of surveyed students are a random sample taken from a population with mean 600 against the alternative that the mean is greater than 600. Assume population standard deviation to be 100. [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.]
Observation: Unlike its predecessors, for paired and two-sample tests, Minitab for the Mac no longer provides the option of comparing the mean of differences, or the difference between means, to any number other than zero. This might be OK for most of us, although it is conceivable that we might want to make other comparisons, such as if dads average more than 2 years older than moms, or if male students' mean weight is over 25 pounds more than female students.
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.
Example P: Use MINITAB to verify that female heights are significantly less than male heights. Procedure may or may not be pooled.
Alternatively, the data may occur in two columns of height values, one for each sex.
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 plot of residuals vs. the explanatory variable (MomAge). Produce a scatterplot showing bands for confidence intervals and prediction intervals. 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.
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.
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 Statistics>ANOVA>One Way and Responses are in one column for all factor levels. In this case, the ``Other" students cannot be omitted.
Example S: Use MINITAB to do inference about the population proportion of males/females. [The following only works for categorical variables like Gender 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.
If a two-way table has been created to summarize the data (as in the Cross Tabulation option) you may enter the counts directly into r rows (where r is the number of possibiities for the explanatory variable) and c columns (where c is the number of possibilities for the response variable) in a Minitab worksheet. For instance, for the first (Female) row enter 32 for the 1st (Year) column, 196 for the 2nd column, 71 for the 3rd, 25 for the 4th, and 7 for Other. For the second (Male) row enter 13, 114, 62, 28, 11, respectively, for the five columns 1st through Other. Then choose Statistics>Tables>Cross Tabulation and Chi-Square and select Summarized data in a two-way table from the drop-down menu. Then enter the five column names 1st through Other in the box Columns containing the table. Request Chi-square test for Association under the Display menu and Click OK.