MINITAB 17 BASICS REFERENCE GUIDE Dr. Nancy Pfenning September 2013 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 Note: After viewing each graph, close it by clicking the red X in the upper right corner; answer no (don't save). 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. 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. 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. 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 Original column orcolumn 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 and ctrl C to copy. Start up MINITAB [or if it's already running, choose File>New>Minitab Worksheet], type ctrl V to paste it. If it asks about delimiters, click OK. 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, 1. Choose Stat>Basic Statistics>Display Descriptive Statistics... 2. Specify Height in the Variables text box. 3. Specify Gender in the By variables text box. 4. Choose Graphs and check Boxplot of data.
5. 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 Height, and genders (M or F) were entered in the column Gender. 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 Height in the Graph variables text box and Gender in the Categorical variables for grouping text box. 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 Height. 3. After "Store samples in:" type the name of a new column, such as SampledHts. Do not check the "sample with replacement" box. 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 625] and standard deviation is assumed to be 100. Use sample scores to obtain a 90% confidence interval for population mean score. 1. Choose Stat>Basic Statistics>1-Sample Z... 2. Specify VerbalSAT in the One or more samples, each in a column box. 3. Click in the Known standard deviation text box and type 100. 4. Select the Options button. 5. Click in the Confidence level text box and type 90. 6. Make sure Alternative is at the default Mean not equal hypothesized mean. 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 VerbalSAT in the One or more samples, each in a column 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 Mean not equal hypothesized mean. 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 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.] 1. Choose Stat>Basic Statistics>1-Sample Z... 2. Specify VerbalSAT in the One or more samples, each in a column box. 3. Click in the Standard deviation text box and type 100. 4. Check the Perform hypothesis test box and enter 600 in the hypothesized mean box. 5. Select the Options button. 6. Under Alternative select Mean greater than hypothesized mean. 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. Under Each sample is in a column click in the Sample 1 text box and specify DadAge. 3. Click in the Sample 2 text box and specify MomAge. 4. Click in the Options button. 5. Make sure the Hypothesized Difference text box says 0.0.
6. Click the arrow button at the right of the Alternative drop-down list box and select Difference greater than hypothesized difference. 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 Both samples are in one column and enter Height for Samples and Gender for Sample IDs... 3. Click in the Options button. 4. Verify that the hypothesized difference is 0.0. 5. Click the arrow button at the right of the Alternative drop-down list box and select Difference less than hypothesized difference (MINITAB considers the difference Females minus Males, with Females first because F comes before M in the alphabet). 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. 8. Click on Graphs and select Boxplot. 9. Click OK. 10. Click OK. Alternatively, the data may occur in two columns of height values, one for each sex. 1. Select the Each sample is in its own column option button if that is the case. 2. Click in the Sample 1 text box and specify FHeights. 3. Click in the Sample 2 text box and specify MHeights. 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 age 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. 5. Choose Stat>Basic Statistics>Correlation... 6. Specify MomAge and DadAge in the Variables text box. 8. Choose Graph>Scatterplot and double-click on With regression. 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>Fit Regression Model. 13. Specify DadAge in the Responses text box. 14. Click in the Continuous 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. 1
19. Click OK. 20. Choose Stat>Regression>Regression>Predict. 21. Verify DadAge appears in the Response text box. 22. Verify MomAge appears below. 23. Type 40 in first line of MomAge box. 2 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. 3. Choose Response data are in a separate column for each factor level. 4. Specify Earned_1, Earned_2, Earned_3, Earned_4 in the Responses text box. 5. Click on the Graphs... box 6. Check the box for Boxplot of 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, usingstat>anova>one Way and Response data are in one column for all factor levels. 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 Gender that have just 2 possibilities.] 1. Choose Graph>Pie Chart and enter Gender as the Categorical variables. 2. Click OK. 3. Choose Stat>Basic Statistics>1Proportion... 4. Choose One or more samples, each in a column. 5. Specify Gender in the box below. 6. Check Perform hypothesis test. 7. Type 0.5 in the Hypothesized proportion box. 8. Click on Options to specify a one-sided alternative or to opt for Method to be a normal approximation. 9. 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 FavoriteColor as the Categorical variables. 2. Click OK. 3. Choose Stat>Tables>Tally Individual Variables. 4. Specify FavoriteColor in the Variables box. 5. Check Counts for Display box. 7. Note the count in the color of interest (events) and the total count N (trials). 8. Choose Stat>Basic Statistics>1Proportion. 9. Choose Summarized data. 10. Specify the numbers of events and trials. 11. Check Perform hypothesis test and type 0.125 as the hypothesized proportion
12. Click on Options and specify a one-sided alternative if you suspected more or fewer than 1/8 would prefer that color. Under Method check "Normal approximation" to make your results consistent with our calculations by hand. 13. Click OK. 1 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. Choose Raw data (categorical variables). 3. Decide which should be the explanatory variable; in this case, it would be Gender. Specify Gender for Rows and Year for Columns 4. For data analysis, check Counts and Row percents under Display. The row percents are conditional percentages for respective values of the explanatory variable. 5. For statistical inference, check the Chi-Square test under the Chi-Square box. 8. Choose Graph>Bar Chart... 9. Double-click on Cluster. 10. Enter Gender and Year as the Categorical variables (Gender first because it is the explanatory variable, graphed horizontally). 11. Click Chart Options. 12. Select Show Y as Percent and Within Categories at level 1 to get side-by-side charts of percentages within each gender group. 13. Click OK. 1 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 Stat>Tables>Chi-Square Test for Association and Summarized data in a two-way table. Then type the five column names 1st through Other in the box Columns containing the table and Click OK. EXAMPLE V Use a two-way table to check for a relationship between two variables. 1. Choose Stat>Tables>Descriptive Statistics. 2. Decide which of the variables should be the explanatory variable; this variable will be for Rows under Categorical Data and the other variable will be for Columns. 3. Under Display summaries for, choose Categorical Variables. 4. Under Display, check mark Counts and Row Percents. 5. Click OK. Your output will be displayed in your Session window.