NG BB 21 Intro to Minitab

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National Guard
Black Belt Training

Module 21

Intro to Minitab

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CPI Roadmap – Measure
8-STEP PROCESS
6. See
1.Validate 2. Identify 3. Set 4. Determine 5. Develop 7. Confirm 8. Standardize
Counter-
the Performance Improvement Root Counter- Results Successful
Measures
Problem Gaps Targets Cause Measures & Process Processes
Through

Define Measure Analyze Improve Control

TOOLS
•Process Mapping
ACTIVITIES
• Map Current Process / Go & See •Process Cycle Efficiency/TOC
• Identify Key Input, Process, Output Metrics •Little’s Law
• Develop Operational Definitions •Operational Definitions
• Develop Data Collection Plan •Data Collection Plan
• Validate Measurement System •Statistical Sampling
• Collect Baseline Data •Measurement System Analysis
• Identify Performance Gaps •TPM
• Estimate Financial/Operational Benefits •Generic Pull
• Determine Process Stability/Capability •Setup Reduction
• Complete Measure Tollgate •Control Charts
•Histograms
•Constraint Identification
•Process Capability
Note: Activities and tools vary by project. Lists provided here are not necessarily all-inclusive. UNCLASSIFIED / FOUO

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Learning Objectives
 Become familiar with the Minitab statistical software
 Learn some of the most widely used tools within
Minitab
 Learn how to navigate within Minitab

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Overview

FEAR NOT!

 Minitab is a good tool for beginners, as well as
established statisticians
 This is just an introduction – Minitab is a powerful tool
that is capable of much more than what we will have
time to cover here
 Suggestion: For now, do not be concerned with all the
bells and whistles
 Minitab Help function is always available and easy to
use

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What Will We Cover?
 We are going to cover just a few of Minitab tools,
including:
 The Minitab layout
 An overview of the menus
 The Minitab Calculator
 Some BASIC statistical tools
 Some BASIC graphing tools
 The HELP menu


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Minitab Layout
 The overall lay out of Minitab is similar to Excel, with
some key differences:
 The worksheet looks similar to a spreadsheet, but
Minitab prefers data to be in columns. An easy way
to think of it is to think of each column as a variable:
Time, speed, date, etc.
 The Session Window is a dialog window. It is where
the non-graphical results of your calculations will be
displayed.


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What Does It Look Like?

Session Window

Worksheet


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Minitab Characteristics
 Although not a Microsoft product, Minitab shares
some characteristics of other programs running on
the Windows platform. In particular, the menu bar
running across the top of the page, and the button
bar below that. We will briefly go through some of
the key functionality in the menu bar. The button bar
contains shortcuts to many of the same functions.
 Also, you can cut and paste data from Excel. You can
do this by simply using the Ctrl-C and Ctrl-V functions
you might already be familiar with.


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Minitab Characteristics
 One of the key differences in Minitab is the distinction
between a Project and a Worksheet
 Worksheets contain your data and nothing else. It is
where you can manipulate the data and store your
data. Minitab will let you have several worksheets
open simultaneously. In addition, worksheets can be
saved independently and shared between projects.
 Projects are more like an entire notebook, containing
not only one or more worksheets, but also your
session window (containing any analysis you have
already performed) and any graphs you have created

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File Menu
Select File

Project
Commands

Worksheet
Commands

Recently
Opened
Worksheets


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Edit Menu
Select Edit The most frequently used feature
found under the Edit drop down
menu is the Edit Last Dialog
which can (and should) be quickly
accessed by using the Control-E
quick key combination.

This feature will allow you to
quickly access the last dialog box
you used and either re-run,
change, or review the last analysis
Select you performed.
Edit Last
Dialog


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Data Menu
Select Data
The Data menu contains several
Column manipulation features.
Two examples are the Column
Select Stack and Unstack features.
Unstack Columns
These features allow you to
or Stack Columns combine multiple columns (or
rows) into one, or break one
column (or row) into multiple
columns (or rows).


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Pizza Example
 Let‟s work through an example of this. Open the
Minitab worksheet called Pizza Exercise.mtw.
 We are going to stack the columns with the different
defect types so that we can make a Pareto chart


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Pizza Example (Cont.)

Step One:
Open data set
Pizza Exercise.mtw


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 First, let's add a couple of headers for the columns
we are about to stack
 The first column we will call “Defect Type” and it
will be where the defect types will be listed. We will
put that header over column C11. The other
column we will call “Count” and that is where we
will put the count for each defect type. We will
place that over column C12.


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Step 2:
Type in the two
new column
headers
C11 = Defect Type
C12 = Count


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 Now, let‟s go to the Data menu:
 Data>Stack>Columns


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Data> Stack>Columns

Step 3:
Select Data,
choose Stack
and finally,
select Columns


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 We are going to stack all the columns that list the different defect
types. That would include Late, Wrong Ingredients, Wrong Order,
Wrong Address, and Damaged. Highlight all these columns and select
them for the “Stack the following columns” box.
 We have a few choices for where to put the new stacked columns.
Remember we created two headers for the new columns: Defect Type
and Count. Since these columns are on the current worksheet, we
select the radio button labeled Column of current worksheet. This will
open up two more fields for us to place the stacked columns.
 In the first field, next to Column of current worksheet, select the
“Count” column. Next, in the field labeled Store subscripts in,
select the “Defect Type” column. Make sure that the box labeled Use
variable names in subscript column is selected. Remember,
Minitab often refers to columns as variables.
 When done, click OK


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Data>Stack Columns
Step 4
Highlight the
columns you want
stacked, then click
“Select” to cause
them to appear here

Step 5
We want the data
stored in the current
worksheet, so select
this Radio Button,
enter data and
click on OK


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 There you go!
 We will come back to this worksheet a little later and
use these stacked columns to create a Pareto chart


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Data>Stack>Columns

Your
Stacked Data
are now
in columns
C11-T & C12


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Calc Menu
 The Calc menu contains a wealth of tools that you will be using
on a regular basis. We will touch on them only briefly here.
 Two such items are the Column Statistics and Row
Statistics features. These items allow you to run simple
calculations such as the sum, mean, median, and standard
deviation on a row or column of data. The result will be
displayed in the session window.
 Two other commonly used items are the Make Patterned
Data, and Random Data functions
 Make Patterned Data will allow creating series, such as
1,2,3,1,2,3 etc. This is good for coding items such as days of
the week.
 Random Data can do a couple of things for you. Not only can
you create a set of random numbers, but this function can also
be used to randomize a column of data.


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Calc Menu (Cont.)

Select Calc
To Get The
Following
Drop-Down Menu


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Calc Menu (Cont.)
 The Calculator is a very commonly used tool. The
power behind the calculator is that it allows us to
perform a wide range of mathematical and statistical
functions not only on a column of data, but between
columns of data.


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The Calculator

The Calculator
Dialog Box


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The Calculator – Example
Let’s Look at an Example:
 Anthony‟s Pizza delivery company was interested in
customer service problems with pizzas that they
delivered. Over a 12-day period, they recorded how
many pizzas they delivered, and how many had defects
that fell into the following categories: Late, Wrong
Ingredients, Wrong Order, Wrong Address, and
Damaged. In addition, they recorded the “Pizza Stylist”
who was making the pizzas each day.
 When they were done collecting the data, they keyed it
into the Minitab worksheet in Columns C4 thru C9.


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The Calculator – Example (Cont.)
 For this example, let‟s say that we want to know how
many total defects we had each day. Since we know
how many of each type of defect we have, all we
need to do is add all of the defect columns together.
 We will use the Calculator to calculate this result,
and put the result in column C10
 Let‟s start by typing the header for column C10 and
calling it “Total Defects”


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Type in the new
column header for
“Total Defects”
in column C-10


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 Now we can have some fun!
 We want to store the result in our new variable “Total Defects”. We
can select this variable in two ways. We can select the column from
the list on the left, then click the Select button. Or we can simply
double click the column from the list.
 It is important to note that to select an item for a particular function,
whether it be the Store the Result in Variable box, or Expression box,
the cursor MUST be in the box you are selecting (or else you might not
see the variables you want to choose).
 Next, we want to create the equation. We would do this exactly the
same as we would using a calculator.
 In the expression box, select Late, then the “+” sign, then Wrong
Ingredients, “+” Wrong Order, etc., until you have selected all the
variables you want to add together. We have already told Minitab
where we want the result stored in the previous step. When you are
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Tell Minitab
where to store the
results
C10 or Total
Defects
Put the formula
to use here:
Late
+ Wrong Ingr.
+ Wrong Order
+ Wrong Address
+ Damaged
Click On OK


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 Voila!
 Column C10 (Total Defects) is now populated with
the data you wanted


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Stat Menu
 The Stat menu is where the majority of Minitab‟s
functionality resides (such as Basic Statistics,
Regression, and Control Charts)
 We are not going to get into a lot of this at this time,
but we will be coming back to visit Basic Statistics and
Pareto charts shortly


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Stat Menu (Cont.)

Select Stat


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Graph Menu
 The Graph menu contains many, many graphs that
can be quite helpful, including Time Series plots,
Histograms, and Boxplots. It should be noted
however, that these graphs are also available in other
places while you are using other tools.
 After selecting a graph type, for many of the graphs,
you will get a Dialog Box which illustrates the various
styles available for the specific graph you have
chosen. (There are some types of graphs that have
only a single style.)


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Graph Menu (Cont.)
Select Graph drop down menu

If you choose Dotplots you would then get
another dialog box showing the various types
of Dotplots

Select Cancel

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Editor Menu
 The Editor menu contains many features you might
already be familiar with due to your exposure with
Excel and other related products. Such features
include Find and Replace.

Selecting Editor


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Window Menu
 The Window menu covers several features that are
common to other Windows-based programs, such as
Tile, and Cascade

Selecting Window

This area
shows currently
open Windows


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Help Menu
 The Help menu is similar to other help menus you
have probably seen
 The main difference is that (as you may have noticed)
Help is also available from all the dialog boxes
 In addition, Minitab has a built in Stat Guide. The
Stat Guide is more centered around the how and why
you use a particular tool and assistance in interpreting
the results.
 The traditional Help menu is more centered around
how to use the software


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Help Menu (Cont.)

Selecting Help


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Basic Statistics

 Let‟s work through our Pizza example to see how to
use Minitab to get some basic information about a
process


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Basic Statistics (Cont.)
 Under Stat>Basic Statistics, there is a small
feature that can provide a wealth of information. We
use it often to get a quick snapshot of the data
underlying the process we are looking at.
 Let‟s say we want to examine the number of defects
our pizza company has been experiencing over the
past 12 days. Now that we are back at our desk, we
are excited to do something with this data to give us
an idea how the process is doing. We might start by
going to:
Stat>Basic Statistics>Graphical Summary


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Stat>Basic Statistics>
Graphical Summary

We want the
Graphical Summary


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 We select the variable we wish to examine – in this
case we are looking at “Total Defects”
 Now, since a picture is worth a hundred words, let‟s
have Minitab show us some graphs along with our
data analysis
 Click the OK button


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Graphical Summary

Double Click on
“Total Defects”
to add it to
the Variables Box

Click on OK


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 To get a good statistical overview of the data, let‟s
take a look at the Graphical Summary. This
summary will include several graphs along with
summary statistics and other useful information.


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Stat>Basic Statistics>Graphical Summary

Summary for Total Defects
A nderson-D arling N ormality Test
A -S quared 0.49
P -V alue 0.174
M ean 10.667
S tD ev 6.110
V ariance 37.333
S kew ness 1.00674
Kurtosis 0.40899
N 12
M inimum 4.000
1st Q uartile 6.000
M edian 8.500
3rd Q uartile 14.750
5 10 15 20 25 M aximum 24.000
95% C onfidence Interv al for M ean
6.784 14.549
95% C onfidence Interv al for M edian
6.000 14.737
95% C onfidence Interv al for S tD ev
95% Confidence Intervals
4.328 10.374
Mean

Median

5.0 7.5 10.0 12.5 15.0


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 First, let‟s look at the graphs that Minitab gives us
 The left side of the graph includes the “pictures” including (from top to
bottom):
 Histogram – Sorts the data into groups, then plots the frequency of
each group along an X-axis and overlays a „Normal‟ distribution
 Boxplot of the data – Breaks the data into quartiles (i.e., 50% of our
data is inside the box)
 95% Confidence Intervals – For our Mean and Median
 The right side of the page contains the calculated data. The main items
you will be using include:
Mean Median Standard Deviation
Minimum Maximum
 In addition there is also a Normality Test and Quartile Information


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Pizza Example Analysis
But what does this mean for our Pizza example? Let’s look at it
piece by piece:
 Histogram – This is showing us that our number of defects vary between 4 and 24
per day. This tells us that there is a great deal of variation in our process.
 Boxplot of the Data – This shows us the same variation, but with some additional
information. For example, we can see that 50% of the pizzas examined in this time
period had between 6 and 14.75 defects, but with an additional 25% having more
than 14.75 defects.

The right side of the page contains some detail about our process
as well. For example:
 The Mean (or average) number of defects on a given day is 10.7 defects, but the
standard deviation is over 6 defects. In other words, on average we create over 10
defects per day, and the variation (as measured by the Standard Deviation) further
amplifies the problem.
 Our Median (the middle value in our data set) is 8.5 defects and the range of our
data (another measure of variation calculated as Max - Min) is a whopping 20
defects!


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Pizza Example Analysis (Cont.)
 What if you do not need the graphs and you just want the data
analysis? You can choose just the data analysis by selecting
Display Descriptive Statistics. This analysis will show up in your
session window. Highlights include:
 N = Sample size
 Mean = Statistical average of the sample
 Median = The “middle point” of the sample
 Standard Deviation = The “average” amount that each data
point deviates from the mean
 Maximum = The largest data point in the sample
 Minimum = The smallest data point in the sample


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Display Descriptive Statistics

Select
for data analysis without the
graphs


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Double click on C-10,
“Total Defects” to add
it to the Variables box

Click on OK to get
your analysis in
the Session Window


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Stat>Basic Statistics>Display Descriptive Statistics

Here is the analysis in the
Session Window


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Stat>Basic Statistics> You can choose which statistics are
Display Descriptive Statistics included by selecting the statistics tab
and checking just the statistics you want

Select Cancel

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 Now let's say we want to compare Total Defects by
Pizza Stylist
 We can do this in the same way we ran Basic
Statistics> Graphical Summary on one variable,
but now we will be examining Multiple Variables all
in one step
 The only difference here is that we need is to select a
“By Variable” that we would like to compare by. In
this case, that would be by “Pizza Stylist.”
 Once again, click OK


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Graphical Summary

Select “Total Defects”
from the list and double
click on it to place it in
the Variables box

Select “Pizza Stylist”
for the
By Variables box

Click On OK


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Stat>
Basic Statistics>
Graphical Summary

This gives you the
Graphical Summary
for all three Pizza Stylists
in One Analysis


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 Minitab will give us the same graphs we requested before, but
this time, we will get one for each pizza stylist (which we have
tiled for this screen shot). This allows us the opportunity to
compare each stylist side by side.
 Again, by looking at the graphs of the pizza stylists, we learn a
lot very quickly, such as:
 Pizza Stylist Bob has much less variation in his process. This can be seen
in the histogram (which is not as spread out as the other stylists), box
plot (which is also much tighter than the other stylists), and the range of
the data (8 defects for Bob, 10 Defects for Sally, and 18 defects for
Frank).
 At this point, we can tell that stylist Bob appears to be more consistent in
minimizing defects. But one thing that is important to note is that we do
not know why. Are they following the same process? Is Bob cutting
corners?
 Also, it should be noted here, that our sample size for this example is too
small to draw statistically significant conclusions


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 Similar to the Graphical Summaries, Minitab will
layout the results for each Pizza Stylist side by side in
the session window. For this we have to select the
option Display Descriptive Statistics. Once again,
we can compare the means, standard deviations, and
ranges for these two associates in fairly short order.
 In addition to the difference in the ranges between
the stylists, one sure sign that Bob has less variation
in his process is his standard deviation – it is much
less than that of the other stylists.


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Stat>Basic Statistics> Display Descriptive Statistics

Multiple Group Data Output
In Session Window

Note: Remember, you get to choose what statistics are included in this analysis


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Boxplots
 Another useful item that we can have Minitab create
are Boxplots
 In addition to being a part of several of Minitab's other
tools, you can find Boxplots under the Graph menu

 The next slide illustrates the Nomenclature and
Various Parts of a Boxplot


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Boxplot Terminology
4.5 Whisker shows range for the
top 25% of the data points.

Outlier
3.5 Third Quartile Line

Median Line
2.5

First Quartile Line
1.5

1 2 Whisker shows range for the
Inter Quartile Range lower 25% of the data points.
(IQR or Box Length)

Inter Quartile Range (IQR) = The distance between the Third Quartile Line and the
First Quartile Line. This includes 50% of your data. Simply stated: IQR = Q3 – Q1.

Outlier = A data point is considered an outlier if it is more than 1.5 x IQR above the
Third Quartile Line or below the First Quartile Line

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Boxplots (Cont.)
 Once again, Boxplot is an area with lots of bells and
whistles, we are just going to create a simple boxplot
 We do this by first selecting Graph>Boxplot
 For the type of Boxplot, select One Y and With Groups,
since we have three groups (or boxes) on the same Y axis
 In the final dialog box select “Total Defects” for the
Graph Variable and choose “Pizza Stylist” for the
By Variable
 Click OK when done


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Boxplots (Cont.)

Select Graph

Select Boxplot


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Boxplots (Cont.)

From Next Dialog Box
Select One Y and With
Groups

Click On OK


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Boxplots (Cont.)

Select “Total Defects”
for the Group Variable

Use “Pizza Stylist” for
the By Variable

Click On OK


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Boxplots (Cont.)

Note: There are no Outliers (asterisks) in this data set

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Boxplots (Cont.)
A boxplot is a very good tool for graphically representing
variation between items you are trying to compare
 The Box Portion of the graph represents where 50% of the
data for that item lies, with the line in the box representing the
median for that item
 The lines on either side of the box are called Whiskers, and
represent the range for the Upper 25% and Lower 25% of
the data. An asterisk outside of the whiskers is commonly
referred to as an Outlier. An outlier is a data point flagged as
unusual, usually because it is significantly outside the range that
Minitab would be expecting to see your data.
 As we can see from these boxplots, stylists Sally and Frank have
significantly more variation in their process, as well as a larger
median


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Time Series Plot

 Another useful chart we often run is called a Time Series
Plot (also commonly referred to as a run chart). As
the name would suggest, this is a useful tool for displaying
how a process is operating over time.
 We find this tool under the Graph menu, labeled
“Time Series Plot”


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Time Series Plots (Cont.)
Select Graph

Select
Time Series Plot


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 With Time Series Plots there are many places for us
to tweak the format of the output and add
enhancements to the graph, but for our example, we
just want a plain and simple Run Chart
 We do this by simply selecting “Total Defects” as
the Series Variable. In addition, if we have a Date
column (as we do here), we can place that under
Time/ Scale.
 The steps needed for a Time Series Plot are shown on
the next few slides


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1. Select Graph and
Time Series Plot

2. Select Simple and
Click on OK


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3. Add “Total Defects”
in the Series box

4. Click on Time/Scale
to add Dates and then
click on OK


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5. Select Radio Button
for Stamp

6. Add “Date” to
Stamp Columns box
and click on OK


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 And here we have it!
 This shows what is happening to our process as time
goes on. Of course, in order for this graph to be of
any value, the data must be entered in time order.
 In our pizza example here, what seems to beg
questioning is “what happened in the period from 9/4
to 9/7 – particularly 9/6 & 9/7?”
 Can you see any other patterns in this data? Hint:
9/1 is a Monday.


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If you hold your Mouse Cursor over any data point, you will get the
corresponding X and Y data values for that point. You can still use the Brush
feature to highlight several data points to see what Rows the data come from.


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Pareto Charts
 Another useful tool for deciding what areas for
improvement should be addressed first are Pareto
Charts
 We can find this tool under Stat>Quality
Tools>Pareto Chart
 The steps needed to do a Pareto Chart for the pizza
data are shown on the next few slides


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Pareto Charts (Cont.)

1. Select Stat>
Quality Tools>
Pareto Chart


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 Remember, we stacked our data into the columns
“Defect Type” and “Count.” These two columns make
up a Defects table that can now be turned into a Pareto
Chart.
 To do this, we click the radio button labeled Chart
defects table and fill in the two fields that are now
available to us. For the first one, our Labels are stored in
the “Defect Type” column. Once you have selected this
for the Labels field, we need to tell Minitab where our
frequency data resides. In this case it is in the column we
called “Count” so select that column. When you have
done that, click OK.


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2. Select Radio Button
for Chart defects table

3. Put “Defects Type”
in Labels in box
and put “Count” in the
Frequencies in box and
click on OK


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 Given the limited resources available for us to address process
improvements, it is important that we concentrate those
resources on the largest problem areas first
 From our Pareto Chart, we can see in short order that Late
Deliveries make up 43.0% of the total defects we are
experiencing, with Wrong Ingredients making up an
additional 29.7%
 This is telling me that the biggest impact on defects will be
made by addressing the portion of the process resulting in Late
Deliveries and (resources and scope permitting) Wrong
Ingredients


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Saving and Exiting

Saving as a Project will save Saving as a Worksheet
the worksheet, session window, will save only the worksheet
report pad, and all graphs (Data Set)

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Takeaways
 Minitab is a powerful tool that, with just a few simple
mouse clicks, can tell you a great deal about your
data including:
Numerical Graphical
 Mean  Histogram
 Median  Box Plots
 Minimum  Time Series Plots
 Maximum  Run Charts
 Range  Pareto Charts
 Standard Variation


UNCLASSIFIED / FOUO

What other comments or questions
do you have?

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APPENDIX

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Minitab Exercise
 This exercise concerns a Customer Service Center. It utilizes the basic Minitab
tools that you have learned and allows you to play with some basic options.
 AHT = Average Handle Time (The average number of seconds required to
complete a call)
 Occupancy = The percentage of time that Customer Service Reps are on the
phone or completing after call work relative to the amount of time they are
logged into the system. Generally the target is 80-85%.
 Speed of Answer = The average number of seconds it takes to answer an
inbound call. Targets vary depending on the required service level but
generally 30 seconds is standard.
 Call Quality Score = “Your call may be recorded for quality purposes.” This
really happens. Calls are recorded for each rep (usually 10-15 per month) and
scored by a dedicated call monitoring team. It is a critical measure for
individual reps.


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Minitab Exercise
 Using the dataset Call Center Data.xls:
1.Copy the data into Minitab
2.Display the distribution of AHT (Average Handle Time) for the
business. What is the mean?
3.On a single graph, display the AHT for sites A, B, and C.
Which site has the least variation?
4.Create the appropriate graph which shows the frequency of
call types. Which call type occurs most often and what
percentage of the overall call volume does it represent?
5.Display the relationship between Occupancy (X) and Speed Of
Answer (Y). What is the nature of the relationship?
6.On a single graph, display the monthly Average Call Quality
Score for Sites A, B, and C. Overall, which site has the
highest scores?

NG BB 21 Intro to Minitab

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