1. INTRODUCTION TO MINITAB

2. Minitab Training Agenda
Worksheet Conventions and Menu Structures
Minitab Interoperability
Graphic Capabilities
Pareto
Histogram
Box Plot
Scatter Plot
Statistical Capabilities
Capability Analysis
Hypothesis Test
Contingency Tables
ANOVA
Design of Experiments (DOE)

3. Worksheet Format and Structure
Menu Bar
Session Window
Tool Bar
Worksheet Data Window

4. (No Additional Designation)
Data Window Column Conventions
Text Column C1-T
(Designated by -T)
Date Column C2-D
(Designated by -D)
Numeric Column C3
(No Additional Designation)

5. Other Data Window Conventions
Data Entry Arrow
Column Names
(Type, Date, Count & Amount
Data Rows
Entered Data for Data Rows 1 through 4

6. Menu Bar - Menu Conventions
Hot Key Available (Ctrl-S)
Submenu Available (… at the end of selection)

7. Menu Bar - File Menu Key Functions Worksheet File Management Save
Print
Data Import

8. Menu Bar - Edit Menu Key Functions Worksheet File Edits Select Delete
Copy
Paste
Dynamic Links

9. Menu Bar - Manip Menu Key Functions Data Manipulation Subset/Split
Sort
Rank
Row Data Manipulation
Column Data Manipulation

10. Menu Bar - Calc Menu Key Functions Calculation Capabilities
Column Calculations
Column/Row Statistics
Data Standardization
Data Extraction
Data Generation

11. Menu Bar - Stat Menu Key Functions
Advanced Statistical Tools and Graphs
Hypothesis Tests
Regression
Design of Experiments
Control Charts
Reliability Testing

12. Menu Bar - Graph Menu Key Functions Data Plotting Capabilities
Scatter Plot
Trend Plot
Box Plot
Contour/3 D plotting
Dot Plots
Probability Plots
Stem & Leaf Plots

13. Menu Bar - Data Window Editor Menu
Key Functions
Advanced Edit and Display Options
Data Brushing
Column Settings
Column Insertion/Moves
Cell Insertion
Worksheet Settings
Note: The Editor Selection is Context Sensitive. Menu selections will vary for:
Data Window
Graph
Session Window
Depending on which is selected.

14. Menu Bar - Session Window Editor Menu
Key Functions
Advanced Edit and Display Options
Font
Connectivity Settings

15. Menu Bar - Graph Window Editor Menu
Key Functions
Advanced Edit and Display Options
Brushing
Graph Manipulation
Colors
Orientation
Font

16. Menu Bar - Window Menu Key Functions Advanced Window Display Options
Window Management/Display
Toolbar Manipulation/Display

17. Menu Bar - Help Menu Key Functions Help and Tutorials Subject Searches
Statguide
Multiple Tutorials
Minitab on the Web

18. MINITAB INTEROPERABILITY

19. Minitab Interoperability
Excel
PowerPoint

20. Starting with Excel...
Load file “Sample 1” in Excel….

21. Starting with Excel...
The data is now loaded into Excel….

22. Starting with Excel...
Highlight and Copy the Data….

23. Move to Minitab...
Open Minitab and select the column you want to paste the data into….

24. Move to Minitab...
Select Paste from the menu and the data will be inserted into the Minitab Worksheet….

25. Use Minitab to do the Analysis...
Lets say that we would like to test correlation between the Predicted Workload and the actual workload….
Select Stat… Regression…. Fitted Line Plot…..

26. Use Minitab to do the Analysis...
Minitab is now asking for us to identify the columns with the appropriate date….
Click in the box for “Response (Y): Note that our options now appear in this box.
Select “Actual Workload” and hit the select button…..
This will enter the “Actual Workload” data in the Response (Y) data field...

27. Use Minitab to do the Analysis...
Now click in the Predictor (X): box…. Then click on “Predicted Workload” and hit the select button… This will fill in the “Predictor (X):” data field...
Both data fields should now be filled….
Select OK...

28. Use Minitab to do the Analysis...
Minitab now does the analysis and presents the results...
Note that in this case there is a graph and an analysis summary in the Session Window…
Let’s say we want to use both in our PowerPoint presentation….

29. Transferring the Analysis...
Let’s take care of the graph first….
Go to Edit…. Copy Graph...

30. Transferring the Analysis...
Open PowerPoint and select a blank slide….
Go to Edit…. Paste Special...

31. Transferring the Analysis...
Select “Picture (Enhanced Metafile)… This will give you the best graphics with the least amount of trouble.

32. Transferring the Analysis...
Our Minitab graph is now pasted into the powerpoint presentation…. We can now size and position it accordingly….

33. Transferring the Analysis...
Now we can copy the analysis from the Session window…..
Highlight the text you want to copy….
Select Edit….. Copy…..

34. Transferring the Analysis...
Now go back to your powerpoint presentation…..
Select Edit….. Paste…..

35. Transferring the Analysis...
Well we got our data, but it is a bit large…..
Reduce the font to 12 and we should be ok…..

36. Presenting the results....
Now all we need to do is tune the presentation…..
Here we position the graph and summary and put in the appropriate takeaway...
Then we are ready to present….

37. Graphic Capabilities

38. Pareto Chart.... Let’s generate a Pareto Chart from a set of data….
Go to File… Open Project…. Load the file Pareto.mpj….
Now let’s generate the Pareto Chart...

39. Pareto Chart....
Go to:
Stat…
Quality Tools…
Pareto Chart….

40. Pareto Chart.... Fill out the screen as follows:
Our data is already summarized so we will use the Chart Defects table...
Labels in “Category”…
Frequencies in “Quantity”….
Add title and hit OK..

41. Pareto Chart....
Minitab now completes our pareto for us ready to be copied and pasted into your PowerPoint presentation….

42. Histogram.... Let’s generate a Histogram from a set of data….
Go to File… Open Project…. Load the file 2_Correlation.mpj….
Now let’s generate the Histogram of the GPA results...

43. Histogram....
Go to:
Graph…
Histogram…

44. Histogram.... Fill out the screen as follows:
Select GPA for our X value Graph Variable
Hit OK…..

45. Histogram....
Minitab now completes our histogram for us ready to be copied and pasted into your PowerPoint presentation….
This data does not look like it is very normal….
Let’s use Minitab to test this distribution for normality…...

46. Histogram.... Go to: Stat… Basic Statistics…
Display Descriptive Statistics….

47. Histogram.... Fill out the screen as follows:
Select GPA for our Variable….
Select Graphs…..

48. Histogram.... Select Graphical Summary…. Select OK…..
Select OK again on the next screen...

49. Histogram....
Note that now we not only have our Histogram but a number of other descriptive statistics as well….
This is a great summary slide...
As for the normality question, note that our P value of .038 rejects the null hypothesis (P<.05). So, we conclude with 95% confidence that the data is not normal…..

50. Histogram....
Let’s look at another “Histogram” tool we can use to evaluate and present data….
Go to File… Open Project…. Load the file overfill.mpj….

51. Histogram....
Go to:
Graph…
Marginal Plot…

52. Histogram.... Fill out the screen as follows:
Select filler 1 for the Y Variable….
Select head for the X Variable
Select OK…..

53. Histogram....
Note that now we not only have our Histogram but a dot plot of each head data as well...
Note that head number 6 seems to be the source of the high readings…..
This type of Histogram is called a “Marginal Plot”..

54. Boxplot....
Let’s look at the same data using a Boxplot….

55. Boxplot.... Go to: Stat… Basic Statistics…
Display Descriptive Statistics...

56. Boxplot.... Fill out the screen as follows:
Select “filler 1” for our Variable….
Select Graphs…..

57. Boxplot.... Select Boxplot of data…. Select OK…..
Select OK again on the next screen...

58. Boxplot....
We now have our Boxplot of the data...

59. Boxplot....
There is another way we can use Boxplots to view the data...
Go to:
Graph…
Boxplot...

60. Boxplot.... Fill out the screen as follows:
Select “filler 1” for our Y Variable….
Select “head” for our X Variable….
Select OK…..

61. Boxplot....
Note that now we now have a box plot broken out by each of the various heads..
Note that head number 6 again seems to be the source of the high readings…..

62. Scatter plot.... Let’s look at data using a Scatterplot….
Go to File… Open Project…. Load the file 2_Correlation.mpj….
Now let’s generate the Scatterplot of the GPA results against our Math and Verbal scores...

63. Scatter plot....
Go to:
Graph…
Plot...

64. Scatter Plot.... Fill out the screen as follows:
Select GPA for our Y Variable….
Select Math and Verbal for our X Variables…..
Select OK when done...

65. Scatter plot....
We now have two Scatter plots of the data stacked on top of each other…
We can display this better by tiling the graphs….

66. Scatter plot....
To do this:
Go to Window…
Tile...

67. Scatter plot....
Now we can see both Scatter plots of the data…

68. Scatter plot....
There is another way we can generate these scatter plots….
Go to:
Graph…
Matrix Plot...

69. Scatter Plot.... Fill out the screen as follows:
Click in the “Graph variables” block
Highlight all three available data sets…
Click on the “Select” button...
Select OK when done...

70. Scatter plot....
We now have a series of Scatter plots, each one corresponding to a combination of the data sets available…
Note that there appears to be a strong correlation between Verbal and both Math and GPA data….

71. Minitab Statistical Tools

72. PROCESS CAPABILITY ANALYSIS

73. Let’s do a process capability study….
Open Minitab and load the file Capability.mpj….

74. SETTING UP THE TEST….
Go to Stat… Quality Tools…. Capability Analysis (Weibull)….

75. SETTING UP THE TEST…. Select “Torque” for our single data column...
Enter a lower spec of 10 and an upper spec of 30. Then select “OK”….

76. INTERPRETING THE DATA….
Note that the data does not fit the normal curve very well...
Note that the Long Term capability (Ppk) is This equates to a Z value of 3*0.43=1.29 standard deviations or sigma values.
This equates to an expected defect rate PPM of 147,055.

77. HYPOTHESIS TESTING

78. Setting up the test in Minitab
Load the file normality.mpj…..

79. Checking the Data for Normality….
It’s important that we check for normality of data samples.
Let’s see how this works….
Go to STAT…. Basic Statistics... Normality Test….

80. Set up the Test We will test the “Before” column of data….
Check Anderson-Darling
Click OK

81. Analyzing the Results
Since the P value is greater than .05 we can assume the “Before” data is normal
Now repeat the test for the “After” Data (this is left to the student as a learning exercise..)

82. Checking for equal variance..
We now want to see if we have equal variances in our samples.
To perform this test, our data must be “stacked”.
To accomplish this go to Manip… Stack… Stack Columns….

83. Checking for equal variance..
Select both of the available columns (Before and After) to stack....
Type in the location where you want the stacked data…. In this example we will use C4….
Type in the location where you want the subscripts stored… In this example we will use C3….
Select OK….

84. Checking for equal variance..
Now that we have our data stacked, we are ready to test for equal variances.…
Go to Stat… ANOVA…. Test for equal Variances...

85. Setting up the test….
Our response will be the actual receipt performance for the two weeks we are comparing. In this case we had put the stacked data in column C4….
Our factors is the label column we created when we stacked the data (C3)..
We set our Confidence Level for the test (95%).
Then select “OK”.

86. Analyzing the data….
Here, we see the 95% confidence intervals for the two populations. Since they overlap, we know that we will fail to reject the null hypothesis.
The F test results are shown here. We can see from the P-Value of .263 that again we would fail to reject the null hypothesis. Note that the F test assumes normality
Note that we get a graphical summary of both sets of data as well as the relevant statistics….
Levene’s test also compares the variance of the two samples and is robust to nonnormal data. Again, the P-Value of .229 indicates that we would fail to reject the null hypothesis.
Here we have box plot representations of both populations.

87. Lets test the data with a 2 Sample t Test
Under Stat… Basic Statistics…. We see several of the hypothesis tests which we discussed in class. In this example we will be using a 2 Sample t Test….
Go to Stat…. Basic Statistics.. 2 Sample t…..

88. Setting up the test….
Since we already have our data stacked, we will load C4 for our samples and C3 for our subscripts.
Since we have already tested for equal variances, we can check off this box…
Now select Graphs….

89. Setting up the test….
We see that we have two options for our graphical output. For this small a sample, Boxplots will not be of much value so we select “Dotplots of data” and hit “OK”. Hit OK again on the next screen….

90. Interpreting the results….
In the session window we have each population’s statistics calculated for us..
Note that here we have a P value of We therefore find that the data does not support the conclusion that there is a significant difference between the means of the two populations...

91. Interpreting the results….
The dotplot shows how close the datapoints in the two populations fall to each other. The close values of the two population means (indicated by the red bar) also shows little chance that this hypothesis could be rejected by a larger sample

92. Paired Comparisons
In paired comparisons we are trying to “pair” observations or treatments. An example would be to test automatic blood pressure cuffs and a nurse measuring the blood pressure on the same patient using a manual instrument.
It can also be used in measurement system studies to determine if operators are getting the same mean value across the same set of samples.
Let’s look at an example: 2_Hypothesis_Testing_Shoe_wear.mpj

93. 2_Hypothesis_Testing_Shoe_wear.mpj
In this example we are trying to determine if shoe material “A” wear rate is different from shoe material “B”.
Our data has been collected using ten boys, whom were asked to wear one shoe made from each material.
Ho: Material “A” wear rate = Material “B” wear rate
Ha: Material “A” wear rate  Material “B” wear rate

94. Paired Comparison
Go to Stat….
Basic Statistics…
Paired t…..

95. Paired Comparison
Select the samples…
Go to Graphs….

96. Paired Comparison Select the Boxplot for our graphical output..
Then select OK..

97. Paired Comparison
We see how the 95% confidence interval of the mean relates to the value we are testing. In this case, the value falls outside the 95% confidence interval of the data mean. This gives us confirmation that the shoe materials are significantly different.

98. CONTINGENCY TABLES (CHI SQUARE)

99. Entering the data….
Enter the data in a table format. For this example, load the file Contingency Table.mpj...

100. Let’s set up a contingency table….
Contingency tables are found under Stat…. Tables… Chi Square Test….

101. Setting up the test….
Select the columns which contain the table. Then select “OK”

102. Performing the Analysis….
Note that you will have the critical population and test statistics displayed in the session window.
Minitab builds the table for you. Note that our original data is presented and directly below, Minitab calculates the expected values.
Here, Minitab calculates the Chi Square statistic for each data point and totals the result. The calculated Chi Square statistic for this problem is

103. ANalysis Of VAriance
ANOVA

104. Let’s set up the analysis
Load the file Anova example.mpj…
Stack the data in C4 and place the subscripts in C5

105. Set up the analysis….
Select Stat…
ANOVA…
One way…

106. Set up the analysis…. Select C4 Responses C5 Factors
Then select Graphs….

107. Set up the analysis….
Choose boxplots of data...
Then OK

108. Analyzing the results….
Note that the P value is less than .05
that means that we reject the null hypothesis

109. Let’s Look At Main Effects….
Choose Stat
ANOVA
Main Effects Plot….

110. Main Effects
Select
C4 Response
C5 Factors OK

111. Analyzing Main Effects..
Formulation 1 Has Lowest Fuel Consumption

112. DESIGN OF EXPERIMENTS (DOE) FUNDAMENTALS

113. First Create an Experimental Design...
Go to
Stat…
DOE…
Factorial...
Create Factorial Design...

114. First Create an Experimental Design...
Select 2 Level Factorial design with 3 factors
Then go to Display Available Designs….

115. Bowling Example (continued)
We can now see the available experimental designs…. We will be using the Full (Factorial) for 3 factors and we can see that it will require 8 runs…
Now, select OK and go back to the main screen.
Once at the main screen select Designs...

116. Bowling Example (continued)
Select your design….
We will be using the Full (Factorial) and again we can see that it will require 8 runs…
Now, select OK and go back to the main screen.
Once at the main screen select Factors...

117. Bowling Example (continued)
Fill in the names for your factors….
Then fill in the actual conditions for low (-) or high (+)
Now, select OK and go back to the main screen.
Once at the main screen select Options...

118. Bowling Example (continued)
Remove the option to Randomize Runs….
Now, select OK and go back to the main screen.
Once at the main screen select OK...

119. Bowling Example (continued)
Minitab has now designed our experiment for us….
Now, type your Data from each of your experimental treatments into C8.
We are now ready to analyze the results…

120. Bowling Example (continued)
Go to
Stat….
DOE…
Factorial...
Analyze Factorial Design...

121. Bowling Example (continued)
Highlight your Data column and use Select to place it in the Responses box.
Then, select the Terms Option.

122. Bowling Example (continued)
Note that Selected Terms has all of the available choices already selected. We need do nothing further.
Select OK.
Then, at the main screen select Graphs

123. Bowling Example (continued)
Select your Effects Plots and reset your Alpha to .05.
Select OK to return to the main screen and then select OK again.

124. Bowling Example (continued)
Note that only one effect has a significance greater than 95%.
All the remaining factors and interactions are not statistically significant.

125. Bowling Example (continued)
Another way we can look at the data is to look at the Factorial Plots of the resulting data.
Go to
DOE….
Factorial…
Factorial Plots….

126. Bowling Example (continued)
Select Main Effects Plot and then Setup…

127. Bowling Example (continued)
Select C8 as your response
Select “Wristband”, “Ball” and “Lane” as your factors.
Then select “OK” and OK again on the main screen.

128. Bowling Example (continued)
The magnitude of the vertical displacement indicates the strength of the main effect for that factor. Here we see that the wristband has dramatically more effect than any other factor. We know from our earlier plots that the wristband is the only statistically significant 95% confidence.
This plot also shows you the direction of the main effects. We clearly see that the “with” condition is related to the higher level of performance.

129. Bowling Example (continued)
Now lets look at the interactions....
Go to
DOE….
Factorial…
Factorial Plots…

130. Bowling Example (continued)
Select InteractionPlot and then Setup…..

131. Bowling Example (continued)
Select C8 as your response variable.
Select “Wristband”, “Ball” and “Lane” as your factors.
Then select “OK” and OK again on the next screen….

132. Bowling Example (continued)
We know from our earlier analysis that none of these interactions were statistically significant for this experiment…..
The more the lines diverge from being parallel, the more the interaction.
We see that the strongest interaction (still not significant) is between the lane and the ball.

133. Bowling Example (Session Window)
This is where Minitab shows us the Main Effects and Interaction Effects..
Note that Wristband has the strongest effect followed by the interaction between the Wristband and the Lane...
You can also see that there is zero error
This is because only 1 run was performed with no replications