Introduction to minitab version 13

©The National Graduate School of Quality Management v.8 • 3 Worksheet Format and Structure Session Window Worksheet Data Window Menu Bar Tool Bar... ©The National Graduate School of Qual

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INTRODUCTION TO

MINITAB VERSION 13

•Worksheet Conventions and Menu Structures

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Worksheet Format and Structure

Session Window

Worksheet Data Window

Menu Bar Tool Bar

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Column Names (Type, Date, Count & Amount

Entered Data for Data Rows 1 through 4

Data Entry Arrow

Data Rows

Other Data Window Conventions

Menu Bar - Menu Conventions

Hot Key Available

(Ctrl-S)

Submenu Available (… at the end of selection)

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Menu Bar - File Menu

Menu Bar - Edit Menu

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Menu Bar - Manip Menu

Row Data Manipulation

Column Data Manipulation

Menu Bar - Calc Menu

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Menu Bar - Stat Menu

Menu Bar - Graph Menu

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Menu Bar - Data Window Editor Menu

Note: The Editor Selection is Context

Sensitive Menu selections will vary for:

Menu Bar - Session Window Editor Menu

Key Functions

•Advanced Edit and Display Options

Font

Connectivity Settings

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Menu Bar - Graph Window Editor Menu

Menu Bar - Window Menu

Key Functions

•Advanced Window Display Options

Window Management/Display

Toolbar Manipulation/Display

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Menu Bar - Help Menu

MINITAB INTEROPERABILITY

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Minitab Interoperability

Excel

Minitab

PowerPoint

Starting with Excel

Load file “Sample 1”

in Excel….

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Starting with Excel

The data is now loaded into Excel….

Starting with Excel

Highlight and Copy the Data….

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Move to Minitab

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

Move to Minitab

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

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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

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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….

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Transferring the Analysis

•Let’s take care of the graph first….

•Go to Edit… Copy Graph

Transferring the Analysis

•Open PowerPoint and select a blank slide….

•Go to Edit… Paste Special

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Transferring the Analysis

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

Transferring the Analysis

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

accordingly…

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Transferring the Analysis

•Now we can copy the analysis from the Session window…

•Highlight the text you want

to copy….

•Select Edit… Copy…

Transferring the Analysis

•Now go back to your powerpoint presentation…

•Select Edit… Paste…

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Transferring the Analysis

•Well we got our data, but it

is a bit large…

•Reduce the font to 12 and

we should be ok…

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….

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Graphic Capabilities

•Now let’s generate the Pareto Chart

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•Now let’s generate the Histogram of the GPA results

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Histogram

•Go to:

•Graph…

•Histogram…

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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…

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Histogram

•Select Graphical Summary….

•Select OK…

•Select OK again on the next screen

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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…

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….

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Histogram

•Go to:

•Graph…

•Marginal Plot…

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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”

Boxplot

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

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Boxplot

•Select Boxplot of data….

•Select OK…

•Select OK again

on the next screen

Boxplot

We now have our Boxplot of the data

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Boxplot

•There is another way we can use Boxplots to view the data

•Go to:

•Graph…

•Boxplot

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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…

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

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Scatter plot

•Go to:

•Graph…

•Plot

•Select OK when done

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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….

Scatter plot

To do this:

•Go to Window…

•Tile

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Scatter plot

Now we can see both Scatter plots

of the data…

Scatter plot

•There is another way we can generate these scatter plots….

•Go to:

•Graph…

•Matrix Plot

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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….

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Minitab Statistical Tools

PROCESS CAPABILITY

ANALYSIS

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Let’s do a process capability study….

Open Minitab and load the file Capability.mpj….

SETTING UP THE TEST….

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

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Select “Torque” for our single data column

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

SETTING UP THE TEST….

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

INTERPRETING THE DATA….

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HYPOTHESIS TESTING

•Load the file normality.mpj…

Setting up the test in Minitab

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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….

Set up the Test

•We will test the

“Before” column of data….

•Check Darling

Anderson-•Click OK

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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 )

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….

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•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….

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

Checking for equal variance

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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”.

•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…

Analyzing the data….

•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

•Here we have box plot

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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.

Setting up the test….

•Since we have already tested for equal

variances, we can check off this box…

•Now select Graphs….

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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….

•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

Interpreting the results….

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•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

Interpreting the results….

Paired Comparisons

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

to determine if operators are getting the same

mean value across the same set of samples.

 Let’s look at an example:

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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.

H o : Material “A” wear rate = Material “B” wear rate

H a : Material “A” wear rate  Material “B” wear rate

Paired Comparison

•Go to Stat…

•Basic Statistics…

• Paired t…

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Paired Comparison

•Select the samples…

•Go to Graphs…

Paired Comparison

•Select the Boxplot for our graphical output

•Then select OK

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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

CONTINGENCY TABLES

(CHI SQUARE)

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Entering the data….

•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…

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•Select the columns which contain the table Then select “OK”

Setting up the test….

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

Performing the Analysis….