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## INTRODUCTION TO MINITAB VERSION 13

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- 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)

Data Window Column Conventions

Text Column C1-T

(Designated by -T)

Date Column C2-D

(Designated by -D)

Numeric Column C3

(No Additional Designation)

Entered Data for Data Rows 1 through 4

Data Rows

Other Data Window Conventions

Data Entry Arrow

Column Names

(Type, Date, Count & Amount

- Key Functions
- Worksheet File Management
- Save
- Data Import

- Key Functions
- Worksheet File Edits
- Select
- Delete
- Copy
- Paste
- Dynamic Links

- Key Functions
- Data Manipulation
- Subset/Split
- Sort
- Rank
- Row Data Manipulation
- Column Data Manipulation

- Key Functions
- Calculation Capabilities
- Column Calculations
- Column/Row Statistics
- Data Standardization
- Data Extraction
- Data Generation

- Key Functions
- Advanced Statistical Tools and Graphs
- Hypothesis Tests
- Regression
- Design of Experiments
- Control Charts
- Reliability Testing

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

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.

Menu Bar - Session Window Editor Menu

- Key Functions
- Advanced Edit and Display Options
- Font
- Connectivity Settings

Menu Bar - Graph Window Editor Menu

- Key Functions
- Advanced Edit and Display Options
- Brushing
- Graph Manipulation
- Colors
- Orientation
- Font

- Key Functions
- Advanced Window Display Options
- Window Management/Display
- Toolbar Manipulation/Display

- Key Functions
- Help and Tutorials
- Subject Searches
- Statguide
- Multiple Tutorials
- Minitab on the Web

Load file “Sample 1” in Excel….

The data is now loaded into Excel….

Highlight and Copy the Data….

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

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

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…..

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...

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...

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….

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

- Open PowerPoint and select a blank slide….
- Go to Edit…. Paste Special...

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

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

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

- Now go back to your powerpoint presentation…..
- Select Edit….. Paste…..

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

- 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….

- 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...

- Go to:
- Stat…
- Quality Tools…
- 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..

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

- 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...

- Go to:
- Graph…
- Histogram…

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

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…...

- Go to:
- Stat…
- Basic Statistics…
- Display Descriptive Statistics….

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

- Select Graphical Summary….
- Select OK…..
- Select OK again on the next screen...

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…..

- 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….

- Go to:
- Graph…
- Marginal Plot…

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

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”..

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

- Go to:
- Stat…
- Basic Statistics…
- Display Descriptive Statistics...

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

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

We now have our Boxplot of the data...

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

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

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…..

- 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...

- Go to:
- Graph…
- 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...

We now have two Scatter plots of the data stacked on top of each other…

We can display this better by tiling the graphs….

- To do this:
- Go to Window…
- Tile...

Now we can see both Scatter plots of the data…

- There is another way we can generate these scatter plots….
- Go to:
- Graph…
- Matrix 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...

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….

Let’s do a process capability study….

Open Minitab and load the file Capability.mpj….

Go to Stat… Quality Tools…. Capability Analysis (Weibull)….

Select “Torque” for our single data column...

Enter a lower spec of 10 and an upper spec of 30. Then select “OK”….

Note that the data does not fit the normal curve very well...

Note that the Long Term capability (Ppk) is 0.43. 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.

Setting up the test in Minitab

- Load the file normality.mpj…..

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….

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

- 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..)

- 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….

- 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….

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

- 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”.

- 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.

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…..

- -

- 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….

- 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….

- In the session window we have each population’s statistics calculated for us..

- Note that here we have a P value of .922. We therefore find that the data does not support the conclusion that there is a significant difference between the means of the two populations...

- 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

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

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

Paired Comparison

- Go to Stat….
- Basic Statistics…
- Paired t…..

Paired Comparison

- Select the samples…
- Go to Graphs….

Paired Comparison

- Select the Boxplot for our graphical output..
- Then select OK..

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.

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

Let’s set up a contingency table….

- Contingency tables are found under Stat…. Tables… Chi Square Test….

- Select the columns which contain the table. Then select “OK”

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 30.846.

ANOVA

- Load the file Anova example.mpj…
- Stack the data in C4 and place the subscripts in C5

- Select Stat…
- ANOVA…
- One way…

- Select
- C4 Responses
- C5 Factors
- Then select Graphs….

- Choose boxplots of data...
- Then OK

Note that the P value is less than .05

that means that we reject the null hypothesis

Let’s Look At Main Effects….

- Choose Stat
- ANOVA
- Main Effects Plot….

Main Effects

- Select
- C4 Response
- C5 Factors
- OK

Analyzing Main Effects..

Formulation 1 Has Lowest Fuel Consumption

First Create an Experimental Design...

- Go to
- Stat…
- DOE…
- Factorial...
- Create Factorial Design...

First Create an Experimental Design...

Select 2 Level Factorial design with 3 factors

Then go to Display Available Designs….

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...

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...

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...

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...

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…

Bowling Example (continued)

- Go to
- Stat….
- DOE…
- Factorial...
- Analyze Factorial Design...

Bowling Example (continued)

Highlight your Data column and use Select to place it in the Responses box.

Then, select the Terms Option.

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

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.

Bowling Example (continued)

Note that only one effect has a significance greater than 95%.

All the remaining factors and interactions are not statistically significant.

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….

Bowling Example (continued)

- Select Main Effects Plot and then Setup…

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.

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 effect @ 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.

Bowling Example (continued)

- Now lets look at the interactions....
- Go to
- DOE….
- Factorial…
- Factorial Plots…

Bowling Example (continued)

- Select InteractionPlot and then Setup…..

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….

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.

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

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