L. Goch – Jan 2011
MINITAB OVERVIEW
AGENDA:
Why do I need Minitab?
 Data Entry & Manipulation
 Minitab Tutorials
 Multi-Vari Charts
 Graphing

Box & Whisker Plots
 Scatter Plots
 Histograms
 Bar Charts

Simple Linear Regression
 Control Charting
 T-tests/F-tests/ANOVA Analyses
 Weibull (‘time to failure’) Plots

WHY DO I NEED MINITAB?:


Minitab:
 Easier / Faster
Graphing
 Graphs & Data
Analysis in 1-Step
 Report Writing tool
within Minitab
 Requires Stacked
Data for most Tools

Excel:
 Easier Data Entry
 No pre-set data
formatting required for
analysis
 Need multiple tools &
Steps for Data
Analysis & Graphs
Minitab & Excel:
 Multiple Windows/Worksheets within one main file.
 Data Analysis Capability
DATA ENTRY & MANIPULATION:

Window Types: Session Window & Data Window
Session Window:
• The Output
Data Window:
• A Worksheet, not an Excel Spreadsheet
• Column names are above first row
• Everything in a column is considered to
be from the same group
DATA ENTRY & MANIPULATION:

Enter Data into Minitab by:
Typing it in
 Cutting & pasting from other programs
 Random number generators in Minitab
 Importing it: Excel, Text, ASCII, Dbase files, etc….

Column Name
Column Headers
T = Text / D = Date
Data Direction Arrow
DATA ENTRY & MANIPULATION:

Graph Manager:

Lists all Created Graphs
DATA ENTRY & MANIPULATION:

Worksheet Manager:

Lists all Created Worksheets
DATA ENTRY & MANIPULATION
Minitab Operating Files
 Minitab has training files in it’s data
subdirectory
 To find them go to:
 C:/Program Files/ MINITAB 14/
English/Sample Data
 Minitab File Extensions
 MPJ for Project Files
 Ex:
Data.mpj
 MTW For Data Files (Worksheets only)
 Ex:
Data.mtw
 MFG For Graphs (Graphs only)
 Ex: Pareto1.mgf

DATA ENTRY & MANIPULATION:
SHORT CUT KEYS
Control C / Control V
 Copies data / Pastes data
 Alt Tab
 Moves you from one Windows application to another
 Ex:
 Minitab to PowerPoint for making presentations
 Excel to Minitab for copying data
 Control E
 Pulls up previous menu
 Control Tab
 Moves you from Data, Session, History, and Info
Windows

DATA ENTRY & MANIPULATION:
COMMON PITFALLS

Numeric / Text / Date Data In Columns

Once a column has been defined with data it will need
to be changed if you want to use a different type of data



Data > Change Data Type
Dollar signs, number signs, and commas are treated as
text data
Grey Menu Screens and Commands
You may be in the wrong Minitab Window
 You may not have finished entering data (Hit the enter
key)


Minitab Moving Slowly

Not enough computer memory

Close some graphs, other applications or upgrade your
computer.
DATA ENTRY & MANIPULATION:
COMMON PITFALLS

Minitab Worksheets do not function the same as
Excel spreadsheets
Graphs may or may not automatically update when
worksheet data is changed. By right clicking over a
graph, you can ‘Update Graph Automatically’ to refresh
the graph to the changes you made in the worksheet
from which it was created.
 Calculated fields within a Minitab worksheet do not
automatically update when changes are made unless
you’ve set the field with a formula



Note: Minitab does not recognize imbedded formulas
imported from other applications
Use caution when closing worksheet or graph windows
as they may be removed without warning

Use Tools > Options to reset these options
MINITAB TUTORIALS

Opening the Tutorial Files

•
Click Help> Tutorials
Open a Session
– Click the plus sign next to Session #
MINITAB TUTORIALS
•
Start by clicking on the Overview and continue to follow step by
step. Minitab will provide you with the already available files so
you can do the analysis along with Minitab.
MINITAB HELP

Opening the Help Files

On any tool, you can Click the
Help button
DATA ENTRY & MANIPULATION EXAMPLE
Most Minitab tools require STACKED DATA
• Unstacked Data needs to be changed
into Stacked Data
Let’s look at some of the Graphs and
Tools that are Available in Minitab.
CHARTS & GRAPHS:
Multi-Vari Charts: Can plot up to 4 different
Factors on a Multi-Vari Chart
Multi-Vari Chart for Actual Measurement by Facility - Type
540
Facility
DHM
DSM
GSO
530
Actual Measurement

520
510
500
490
480
470
Calibration
Production
Type
CHARTS & GRAPHS:

Box & Whisker Plot: Same data, different view.
50% of data is within the BOX.
Mechanized Card Reader: All Part Measurements > 170
November 2010 Production Data
Actual Measurement
600
515.6
505.7
500
474.6
400
300
200
170
DHM
Dashed Lines are Tester Adj. Specifications
DSM
Facility
GSO
CHARTS & GRAPHS:

Histogram: Same data, different view.
Mechanized Card Reader: All Part Measurements > 170
November 2010 Production Data
18
F acility
DHM
DS M
GSO
16
14
Percent
12
10
8
6
4
2
0
180
240
300
360
420
Actual Measurement
480
540
600
CHARTS & GRAPHS:

Capability Chart: Same data, different view.
GSO Production Data: November 2010
1 or more Measurements per Part
LSL
USL
P rocess Data
LS L
468
Target
*
USL
568
S ample M ean 516.805
S ample N
635
S tDev (Within) 20.0153
S tDev (O v erall) 26.0809
P otential (Within) C apability
Cp
0.83
C P L 0.81
C P U 0.85
C pk 0.81
O v erall C apability
Pp
PPL
PPU
P pk
C pm
420
O bserv ed P erformance
P P M < LS L 11023.62
P P M > U S L 42519.69
P P M Total 53543.31
450
E xp. O v erall P erformance
P P M < LS L 30652.77
P P M > U S L 24826.30
P P M Total
55479.07
480
510
540
570
0.64
0.62
0.65
0.62
*
CHARTS & GRAPHS:

Bar Chart: Same data, different view.
Mechanized Card Reader Nov 2010 Production Data
Avg Times Card Reader Measured: DSM=1.8, DHM = 1.4, GSO=1.1
100
98.0%
91.7%
80
60
40
20
P ercent w ithin lev els of F acility ; Doesn't include V alues < 170
0
0
1 hr to 1 day
> 1 day
5-60 min
1-5 min
1.8%
GSO
w/i 1 min
0 0.2%
1 Meas
> 1 day
1 hr to 1 day
0 0.5% 0.1%
5-60 min
1-5 min
DSM
1 Meas
> 1 day
1 hr to 1 day
5-60 min
1-5 min
w/i 1 min
2.0%
DHM
Facility
1 Meas
0
Meas Grps
13.2%
3.0%
2.3%
1.0%
1.1%
0.9%
w/i 1 min
Percent of C ard Readers
84.2%
CHARTS & GRAPHS:

Control Chart: Daily Calibration Data.
Nov 2010 Daily Calibration Part Measurements
Meas Error estimated from Calibration Checks
DHM: D0050772
DHM
DSM: D0050693
DSM
GSO: D0050148
GSO
_
_
X=538.8
X-Bar Chart
600
_
_
X=511.8
_
_
X=500.8
500
1
400
Nov-02
1
Nov-09
Nov-19
Nov-29
DHM K0050772
DHM:
Nov-04
Nov-13
Date
Nov-26
DSM D0050693
DSM:
Meas Error: +/- 100.2
Nov-03
Nov-09
Nov-16
Nov-23
GSO D0050148
GSO:
Meas Error: +/- 88.8
Meas Error: +/- 25.8
R-Chart
200
1
_
R=43.9
100
_
R=38.9
1
_
R=11.3
1
0
Nov-02
Nov-09
Nov-19
Nov-29
Tests performed w ith unequal sample sizes
Nov-04
Nov-13
Date
Nov-26
Nov-03
Nov-09
Nov-16
Nov-23
CHARTS & GRAPHS:
Control Chart: Daily Production Data.

Mechanized Card Reader: Final Part Measurements
November 2010 Production Data
DHM
DSM
Xbar Chart
550
GSO
_
_
X=497.1
1
1
_
_
1
_
_
X=483.5
1
1
500
1
1
1
1
1 X=514.8
1
1
1
1
450
11/2/2010
11/8/2010
11/24/2010
11/30/2010
DHM
11/5/2010
11/16/2010
Date
11/30/2010
DSM
11/5/2010
11/11/2010
11/19/2010
11/30/2010
GSO
60
S Chart
_
S=25.21
_
S=17.57
40
_
S=19.31
20
0
11/2/2010
11/8/2010
11/24/2010
11/30/2010
Tests performed w ith unequal sample sizes
11/5/2010
11/16/2010
Date
11/30/2010
11/5/2010
11/11/2010
11/19/2010
11/30/2010
BASIC DATA ANALYSIS:

Two Sample T-Test: Compares Avgs of 2 Groups
Results for: By Load Data
Two-Sample T-Test and CI: % Total Late Dbl Bills, FW
Two-sample T for % Total Late Dbl Bills
FW N Mean StDev SE Mean
30.20 8 0.00312 0.00372 0.00130
40.50 8 0.00063 0.00116 0.00041
Difference = mu (30.20) - mu (40.50)
Estimate for difference: 0.00250
95% CI for difference: (-0.00045, 0.00545)
T-Test of difference = 0 (vs not =): T-Value = 1.81 P-Value = 0.091 DF = 14
Both use Pooled StDev = 0.0028
BASIC DATA ANALYSIS:

F-Test: Compares Stdevs of >2 Groups
Test for Equal Variances for % Total Late Dbl Bills
F-Test
Test Statistic
P-Value
30.20
10.33
0.006
FW
Levene's Test
Test Statistic
P-Value
40.50
0.000
0.002
0.004
0.006
95% Bonferroni Confidence Intervals for StDevs
0.20%
0.40%
0.60%
% Total Late Dbl Bills
0.008
FW
30.20
40.50
0.00%
0.80%
1.00%
3.32
0.090
BASIC DATA ANALYSIS:
Chi-Squared: Compares Counts of >2 Groups

Chi-Squared Analysis: Chart of Observed and Expected Values
10
N
12
DF
1
Chi-Sq
5.33333
Late Double Bills
8
6
4
2
Category
0
30.20
40.50
P-Value
0.021
E xpected
O bserv ed
BASIC DATA ANALYSIS:

Data Analysis: Analyzes DOE & Non-DOE Data
WEIBULL PLOTS:
AFD 1.6 Data: 1 Failure at 1.8 M cycles, 1 Failure at
2.2 M cycles, 1 still going at 1.3 M cycles
AFD 1.6 Probability Plot
Weibull - 90% CI
Censoring Column in Censor - LSXY Estimates
1.3
99
Percent

Table of Statistics
Shape
5.93053
Scale
2.13611
Mean
1.98043
StDev
0.387894
Median
2.00809
IQR
0.525705
Failure
2
Censor
1
AD*
4.916
Correlation
1.000
90
80
70
60
50
40
30
20
10
5
3
2
1
By 1.3 M Cycles
5.1% of Parts will
have Failed
1.0
1.5
Millions of Cycles
2.0
2.5
3.0
QUESTIONS???