1. Statistical Quality & Process Control
2/18/2019
IENG Lecture 09
Examples of Hypothesis Tests:
Anthropometric Data and Intro to the Seven Tools of Ishikawa
2/18/2019
IENG 486: Statistical Quality & Process Control
(c) , D.H. Jensen & R. C. Wurl

2. Statistical Quality & Process Control
2/18/2019
Assignment:
Preparation:
Print Hypothesis Test Tables from Materials page
Have this available in class …or exam!
Reading:
Chapter 5:
5.1 through 5.2, and only these portions are on Exam I
HW 3:
CH 5: # 5, 26, 27
Review for Exam I
2/18/2019
IENG 486: Statistical Quality & Process Control
(c) , D.H. Jensen & R. C. Wurl

3. Grip Strength Data Results
R-L Side, Equal Variance Dominant Hand Means Comparison:
L = x1 = 129.4, S12 = 2788,
n1 = 34 people
R = x2 = 104.0, S22 = 1225,
n2 = 20 people
Sp = 47.1, v = 52
Two-Sided Test at  = .05
HA: There is a difference
Test: Is | t0 | > t.025, 52?
|1.91| > NO!
Keep the Null Hypothesis:
There is NOT a difference btwn
L & R !
2/18/2019
IENG 486: Statistical Quality & Process Control

4. Grip Strength Data Results
R-L Side, No Assumptions Dom. Hand Means Comparison:
L = x1 = 129.4, S12 = 2788,
n1 = 34 people
R = x2 = 104.0, S22 = 1225,
n2 = 20 people
v = 51
Two-Sided Test at  = .05
HA: There is a difference
Test: Is | t0 | > t.025, 51?
|2.12| > YES!
Reject the Null Hypothesis:
There IS a difference btwn
L & R!
Why is this wimpy test
significant when the other wasn’t?
ANS: Check the equal
variance assumption!
2/18/2019
IENG 486: Statistical Quality & Process Control

5. Grip Strength Data Results
Unknown 0
Variances Comparison:
S12 = 2788
n1 = 34, v1 = 33
S22 = 1225
n2 = 20, v2 = 19
Two-Sided Test at  = .10
HA: There is a difference
Test: Is F0 > F.05, 33, 19?
2.276 > YES!
(if not, also check F1– /2, 33, 19)
Reject the Null Hypothesis:
There IS a difference in variance!
At  = .05, this test is just barely not significant
(Should also have checked for Normality with Normal Prob. Plot)
2/18/2019
IENG 486: Statistical Quality & Process Control

6. Statistical Quality Improvement
Goal: Control and Reduction of Variation
Causes of Variation:
Chance Causes / Common Causes
In Statistical Control
Natural variation / background noise
Assignable Causes / Special Causes
Out of Statistical Control
Things we can do something about - IF we act quickly!
Both can cause defects – because specifications are often set regardless of process capabilities!
2/18/2019
IENG 486: Statistical Quality & Process Control

7. Ishikawa’s “Magnificent Seven” Tools
The Seven Tools are:
Histogram / Stem & Leaf Diagram
Cause & Effect (Fishbone) Diagram
Defect Concentration Diagram
Check Sheet
Scatter (Plot) Diagram
Pareto Chart
Control Chart - covered after exam!
The tools were not invented by Ishikawa, but were very successfully put into methodical use by him
The first six are used before starting to use the seventh
They are also reused when needed to find an assignable cause
2/18/2019
IENG 486: Statistical Quality & Process Control

8. Ishikawa’s Tools: Histogram
A histogram is a bar chart that takes the shape of the distribution of the data. The process for creating a histogram depends on the purpose for making the histogram.
One purpose of a histogram is to see the shape of a distribution. To do this, we would like to have as much data as possible, and use a fine resolution.
A second purpose of a histogram is to observe the frequency with which a class of problems occurs. The resolution is controlled by the number of problem classes. – see Pareto Chart slide!
2/18/2019
IENG 486: Statistical Quality & Process Control

9. Ishikawa’s Tools: Fishbone Diagram
Cause & Effect diagram constructed by brainstorming
Identified problem at the “head”
Connects potential causes along the spine
Sub-causes are listed along the major “bones”
Man
Material
Method
Machine
Environment
2/18/2019
IENG 486: Statistical Quality & Process Control

10. Cause & Effect Diagram, Cont.
The purpose of the cause and effect diagram is to obtain as many potential influencers of a process, so that the problem solving can take a more directed approach.
Material
Machine
Man
Method
Environment
Skill Level
Low RPM
Orifice Clogs
Dusty
Humidity
Poor Conductor
Attention Level
Poor Mixing
Travel Limits
Temperature
Worn Parts
Poor Vendor
Bad Paint
2/18/2019
IENG 486: Statistical Quality & Process Control

11. Ishikawa’s Tools: Defect Diagram
A defect concentration diagram graphically records the frequency of a defect with respect to product location.
Obtain a digital photo or multi-view part print showing all product faces.
Operator tallies the number and location of defects as they occur on the diagram.
2/18/2019
IENG 486: Statistical Quality & Process Control

12. Ishikawa’s Tools: Check Sheet
Title
Header Info: Date, Time, Location, Operator, etc.
Types of Errors
Times of Occurrence (periodic)
Type of Error Statistics
Time of Occurrence Statistics
Overall Statistics
Instructions, settings, comments, etc.
Raw Data recorded here
Check sheets are used to collect data (values or pieces of information) in a consistent manner.
List each of the known / possible problems
Record each occurrence including time-orientation.
2/18/2019
IENG 486: Statistical Quality & Process Control

13. Ishikawa’s Tools: Scatter Plot
A scatter plot shows the relationship between any two variables of interest:
Plot one variable along the X-axis and the other along the Y-axis
The presence of a relationship can be inferred or ruled out, but it cannot determine if a cause and effect relationship exists Y X
2/18/2019
IENG 486: Statistical Quality & Process Control

14. Ishikawa’s Tools: Pareto Chart
80% of any problem is the result of 20% of the potential causes
Histogram categories are sorted by the magnitude of the bar
A line graph is overlaid, and depicts the cumulative proportion of defects
Quickly identifies where to focus efforts
2/18/2019
IENG 486: Statistical Quality & Process Control

15. Use of Ishikawa’s Tools
Reduce Variability
Identify Special Causes - Good (Incorporate)
Improving Process Capability and Performance
Characterize Stable Process Capability
Head Off Shifts in Location, Spread
Identify Special Causes - Bad (Remove)
Continually Improve the System
Statistical Quality Control and Improvement
Time
Center the Process
LSL  USL
Removing special causes of variation
Preparation for:
hypothesis tests
control charts
process improvement
2/18/2019
IENG 486: Statistical Quality & Process Control