Chapter 7
Control Charts for Attributes
LEARNING OBJECTIVES
After completing this chapter you should be able to:
1. Understand the statistical basis of attributes control charts
2. Know how to design attributes control charts
3. Know how to set up and use

Lecture 9
Sampling Techniques
Chapter 15
1
The Acceptance-Sampling
Problem
Acceptance sampling is concerned
with inspection and decision making
regarding products.
Three aspects of sampling are
important:
2
Involves random sampling of an entire
lot
Accep

Lecture 6
Control Charts for
Attributes
Introduction
Data that can be classified into one of
several categories or classifications is
known as attribute data.
Classifications such as conforming and
nonconforming are commonly used in
quality control.
An

Introduction to Experimental
Design
Lecture 11
Design of Experiments
Experimental Design is a method for
evaluating the effects of different
treatments on a response variable
aka: Design of Experiments (DOE)
Treatments are generally considered
the diff

Lecture II
Probability
1
Definitions
Population: the set of all possible outcomes
Sample: some part or subset of the
population
Statistical Experiment: any phenomenon
in which the outcome is uncertain
Sample Space: set of all possible outcomes
in an e

Industrial Quality Control
IE 672
1
Course Expectations
Moodle will be used for this class
Course Work:
2 Exams Midterm + Final
Homework assigned to be handed in
electronically
Class Participation
1 Research Project to be assigned the
second half of

Lecture 8
Process and Measurement System
Capability
Process Capability
Process capability refers to the uniformity of the
process.
Variability in the process is a measure of the
uniformity of output.
Two types of variability:
Natural or inherent variabil

Homework #3
IE672
4.1
4.12
a.)
One-Sample T: 4.12
Test of mu = 12 vs > 12
Variable
4.12
N
10
Mean
12.0150
StDev
0.0303
SE Mean
0.0096
95% Lower
Bound
11.9974
T
1.57
P
0.076
Cannot reject the Ho: mean=12 and conclude that mean is greater than 12.
b.)
One-S

Hw#11
IE672
4-35
a.)
One-way ANOVA: Ex4-35Obs versus Ex4-35Flow
Source
Ex4-35Flow
Error
Total
S = 0.7132
DF
2
15
17
Level
125
160
200
N
6
6
6
SS
3.648
7.630
11.278
MS
1.824
0.509
R-Sq = 32.34%
Mean
3.3167
4.4167
3.9333
StDev
0.7600
0.5231
0.8214
F
3.59
P

Chapter 9
Cumulative Sum and Exponentially Weighted
Moving Average Control Charts
LEARNING OBJECTIVES
After completing this chapter you should be able to:
1. Set up and use CUSUM control charts for monitoring the process mean
2. Design a CUSUM control cha

Kathryn Sichler
HW#4
IE672
5.17
There are no runs of more than 5 points and no cycles. It appears random.
5.18
There are no runs of more than 5 points and no cycles. It appears random. There is one point very close
to the control limit however. This may t

University of Tennessee, Knoxville
Trace: Tennessee Research and Creative
Exchange
Masters Theses
Graduate School
5-2010
A Call Center Simulation Study: Comparing the
Reliability of Cross-Trained Agents to Specialized
Agents
Louis Franklin Ali III
Univers

Understanding the Margin of Errors
and the Coefficient of Variance in the
American Community Survey
U.S. Census Bureau
Workshop at SACOG
Michael Burns
Deputy Regional Director
American Community Survey
Four Main Types of Characteristics of the Population

Gianpiero Geranio
10/27/2016
CH 4 Model
Prof. Bengu
The create module make entities that enter the system. Entities are created by entering a
schedule or time between arrivals, also entities per arrival can be typed in. For Part A enter
EXPO for type, 5 f

Lecture 10
Acceptance Sampling
Part II
Designing Sampling Plan with
Specified OC Curve
For single sample attributes plan, use
Binomial nomograph on p 661.
Eg. Design a single sampling plan with
AQL = 2% and LTPD = 8%.
Recall AQL corresponds to Pa =.95
R

Chapter 4
Inferences About Process Quality
IE 672 Lecture 3
1
Statistics and Sampling
Distributions
Statistical methods are used to make
decisions about a process
Is the process out of control?
Is the process average you were given the true
value?
Wha

NEW JERSEY INSTITUTE OF TECHNOLOGY
Department of Mechanical & Industrial Engineering
Project 1
IE 672
Industrial Quality Control
Fall 2014
In semiconductor manufacturing, a hard-bake process follows the photolithography step. An important flow width
of th

Lecture 4
Introduction to SPC
1
Control Charts
Control Charts: a statistical tool used to
distinguish between common cause variation and
special or assignable causes of variation.
X-bar Chart f or Data
Sample Mean
11.5
UCL=11.49
10.5
Mean=10.07
9.5
LCL=8

Lecture 12
Factorial and Fractional Factorial
Experiments for Process Design
and Improvement
2k Factorial Design
The 22 Design
2k is the notation used to indicate that
a certain experimental design has k
factors of interest, each at two levels.
22 desig

Lecture 7
Other Control Chart Topics
Demerit Systems
When several less severe or
minor defects can occur, we may
need some system for classifying
nonconformities or defects
according to severity; or to weigh
various types of defects in some
reasonable man

Lecture 5
Control Charts for Variables
1
Introduction
Variable - a single quality characteristic that
can be measured on a numerical scale.
When working with variables, we should
monitor both the mean value of the
characteristic and the variability asso