INTRODUCTION TO
MINITAB VERSION 13
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)
Worksheet Format and Structure
Menu Bar
Session Window
Worksheet Data Window
Tool Bar
Data Window Column Conventions
Text Column C1-T
Date Column C2-D
(Designated by -T)
(Designated by -D)
Numeric Column C3
(No Additional Designation)
Other Data Window Conventions
Data Entry Arrow
Column Names
(Type, Date, Count & Amount
Data Rows
Entered Data for Data
Rows 1 through 4
Menu Bar - Menu Conventions
Hot Key Available
(Ctrl-S)
Submenu Available (…
at the end of selection)
Menu Bar - File Menu
Key Functions
•Worksheet File Management
Save
Print
Data Import
Menu Bar - Edit Menu
Key Functions
•Worksheet File Edits
Select
Delete
Copy
Paste
Dynamic Links
Menu Bar - Manip Menu
Key Functions
•Data Manipulation
Subset/Split
Sort
Rank
Row Data Manipulation
Column Data Manipulation
Menu Bar - Calc Menu
Key Functions
•Calculation Capabilities
Column Calculations
Column/Row Statistics
Data Standardization
Data Extraction
Data Generation
Menu Bar - Stat Menu
Key Functions
•Advanced Statistical Tools and Graphs
Hypothesis Tests
Regression
Design of Experiments
Control Charts
Reliability Testing
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
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
Menu Bar - Window Menu
Key Functions
•Advanced Window Display Options
Window Management/Display
Toolbar Manipulation/Display
Menu Bar - Help Menu
Key Functions
•Help and Tutorials
Subject Searches
Statguide
Multiple Tutorials
Minitab on the Web
MINITAB
INTEROPERABILITY
Minitab Interoperability
Minitab
Excel
PowerPoint
Starting with Excel...
Load file “Sample 1”
in Excel….
Starting with Excel...
The data is now
loaded into Excel….
Starting with Excel...
Highlight and
Copy the Data….
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….
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….
•This will enter the “Actual
Workload” data in the
Response (Y) data field...
•Click in the box for
“Response (Y): Note that our
options now appear in this box.
•Select “Actual Workload” and
hit the select button…..
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….
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...
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….
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…..
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….
Graphic Capabilities
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...
Pareto Chart....
•Go to:
•Stat…
•Quality Tools…
•Pareto Chart….
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..
Pareto Chart....
Minitab now completes
our pareto for us ready to
be copied and pasted into
your PowerPoint
presentation….
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...
Histogram....
•Go to:
•Graph…
•Histogram…
Histogram....
Fill out the screen as
follows:
•Select GPA for our X
value Graph Variable
•Hit OK…..
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…...
Histogram....
•Go to:
•Stat…
•Basic Statistics…
•Display Descriptive
Statistics….
Histogram....
Fill out the screen as
follows:
•Select GPA for our
Variable….
•Select Graphs…..
Histogram....
•Select Graphical
Summary….
•Select OK…..
•Select OK again on
the next screen...
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….
Histogram....
•Go to:
•Graph…
•Marginal Plot…
Histogram....
Fill out the screen as
follows:
•Select filler 1 for the
Y Variable….
•Select head for the X
Variable
•Select OK…..
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….
Boxplot....
•Go to:
•Stat…
•Basic Statistics…
•Display Descriptive
Statistics...
Boxplot....
Fill out the screen as
follows:
•Select “filler 1” for
our Variable….
•Select Graphs…..
Boxplot....
•Select Boxplot of
data….
•Select OK…..
•Select OK again
on the next
screen...
Boxplot....
We now have our
Boxplot of the data...
Boxplot....
•There is another way we
can use Boxplots to view
the data...
•Go to:
•Graph…
•Boxplot...
Boxplot....
Fill out the screen as
follows:
•Select “filler 1” for
our Y Variable….
•Select “head” for our
X Variable….
•Select OK…..
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...
Scatter plot....
•Go to:
•Graph…
•Plot...
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...
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...
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...
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...
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….
Minitab Statistical Tools
PROCESS CAPABILITY
ANALYSIS
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)….
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”….
INTERPRETING THE DATA….
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.
HYPOTHESIS TESTING
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….
Set up the Test
•We will test the
“Before” column of
data….
•Check AndersonDarling
•Click OK
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….
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….
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...
Setting up the test….
•Our factors is the label
column we created when we
stacked the data (C3)..
•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….
•We set our Confidence
Level for the test (95%).
•Then select “OK”.
Analyzing the data….
•Note that we get a
graphical summary of
both sets of data as
well as the relevant
statistics….
•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.
•Here we have box plot
representations of both
populations.
•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
•Levene’s test also
compares the variance
of the two samples and
is robust to nonnormal
data. Again, the PValue of .229 indicates
that we would fail to
reject the null
hypothesis.
Lets test the data with a 2 Sample t Test
•Under Stat… Basic
- several
Statistics…. We see
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…..
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….
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….
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 .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….
•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.
CONTINGENCY TABLES
(CHI SQUARE)
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….
Setting up the test….
•Select the columns
which contain the table.
Then select “OK”
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 30.846.
ANalysis Of VAriance
ANOVA
Let’s set up the analysis
•Load the file Anova
example.mpj…
•Stack the data in C4 and
place the subscripts in C5
Set up the analysis….
•Select Stat…
•ANOVA…
•One way…
Set up the analysis….
•Select
• C4 Responses
• C5 Factors
•Then select Graphs….
Set up the analysis….
•Choose boxplots
of data...
•Then OK
Analyzing the results….
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..
Main Effects Plot - Data Means for Liters Per H
22
Liters Per H
21
20
19
18
Liters/Hr 1
Liters/Hr 2
Liters/Hr 3
Formulation
Formulation 1 Has Lowest Fuel Consumption
DESIGN OF
EXPERIMENTS (DOE)
FUNDAMENTALS
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