Оглавление
Bhisham C. Gupta. Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Statistics and Probability with Applications for Engineers and Scientists Using Minitab, R and JMP
Copyright
Preface. AUDIENCE
MOTIVATION
HISTORY
APPROACH
WHAT IS NEW IN THIS EDITION
HALLMARK FEATURES. Software Integration
Breadth of Coverage
STUDENT RESOURCES
INSTRUCTOR RESOURCES
Acknowledgments
About The Companion Site
Chapter 1 Introduction
1.1 Designed Experiment
1.1.1 Motivation for the Study
1.1.2 Investigation
1.1.3 Changing Criteria
1.1.4 A Summary of the Various Phases of the Investigation. Phase a
Phase b
Phase c
Phase d
Phase e
1.2 A Survey
1.3 An Observational Study
1.4 A Set of Historical Data
1.5 A Brief Description of What is Covered in this Book
Chapter 2 Describing Data Graphically and Numerically
Topics Covered
Learning Outcomes
2.1 Getting Started with Statistics. 2.1.1 What Is Statistics?
2.1.2 Population and Sample in a Statistical Study
Definition 2.1.1
Definition 2.1.2
Definition 2.1.3
Definition 2.1.4
Definition 2.1.5
Definition 2.1.6
Definition 2.1.7
2.2 Classification of Various Types of Data
2.2.1 Nominal Data
2.2.2 Ordinal Data
2.2.3 Interval Data
2.2.4 Ratio Data
PRACTICE PROBLEMS FOR SECTIONS 2.1 AND 2.2
2.3 Frequency Distribution Tables for Qualitative and Quantitative Data
2.3.1 Qualitative Data
Solution: MINITAB
2.3.2 Quantitative Data
MINITAB
PRACTICE PROBLEMS FOR SECTION 2.3
2.4 Graphical Description of Qualitative and Quantitative Data. 2.4.1 Dot Plot
2.4.2 Pie Chart
2.4.3 Bar Chart
2.4.4 Histograms
Definition 2.4.1
MINITAB
2.4.5 Line Graph
2.4.6 Stem‐and‐Leaf Plot
Definition 2.4.2
MINITAB
PRACTICE PROBLEMS FOR SECTION 2.4
2.5 Numerical Measures of Quantitative Data
Definition 2.5.1
Definition 2.5.2
2.5.1 Measures of Centrality
Mean
Median
Solution:
Solution:
Weighted Mean
Mode
Definition 2.5.3
Definition 2.5.4
Definition 2.5.5
2.5.2 Measures of Dispersion
Range
Variance
Standard Deviation
Empirical Rule
MINITAB
PRACTICE PROBLEMS FOR SECTION 2.5
2.6 Numerical Measures of Grouped Data
Definition 2.6.1
2.6.1 Mean of a Grouped Data
2.6.2 Median of a Grouped Data
2.6.3 Mode of a Grouped Data
2.6.4 Variance of a Grouped Data
PRACTICE PROBLEMS FOR SECTION 2.6
2.7 Measures of Relative Position
2.7.1 Percentiles
2.7.2 Quartiles
2.7.3 Interquartile Range (IQR)
2.7.4 Coefficient of Variation
MINITAB
2.8 Box‐Whisker Plot
2.8.1 Construction of a Box Plot
2.8.2 How to Use the Box Plot. About the Outliers
About the Shape of the Distribution
MINITAB
PRACTICE PROBLEMS FOR SECTIONS 2.7 AND 2.8
2.9 Measures of Association
MINITAB:
PRACTICE PROBLEMS FOR SECTION 2.9
2.10 Case Studies
2.10.1 About St. Luke's Hospital
2.11 Using JMP
Review Practice Problems
Notes
Chapter 3 Elements of Probability
Topics Covered
Learning Outcomes
3.1 Introduction
3.2 Random Experiments, Sample Spaces, and Events. 3.2.1 Random Experiments and Sample Spaces
3.2.2 Events
3.3 Concepts of Probability
PRACTICE PROBLEMS FOR SECTIONS 3.2 AND 3.3
3.4 Techniques of Counting Sample Points
3.4.1 Tree Diagram
3.4.2 Permutations
3.4.3 Combinations
3.4.4 Arrangements of n Objects Involving Several Kinds of Objects
PRACTICE PROBLEMS FOR SECTION 3.4
3.5 Conditional Probability
3.6 Bayes's Theorem
PRACTICE PROBLEMS FOR SECTIONS 3.5 AND 3.6
3.7 Introducing Random Variables
Review Practice Problems
Chapter 4 Discrete Random Variables and Some Important Discrete Probability Distributions
Topics Covered
Learning Outcomes
4.1 Graphical Descriptions of Discrete Distributions
4.2 Mean and Variance of a Discrete Random Variable. 4.2.1 Expected Value of Discrete Random Variables and Their Functions
Proof
4.2.2 The Moment‐Generating Function‐Expected Value of a Special Function of
PRACTICE PROBLEM FOR SECTIONS 4.1 AND 4.2
4.3 The Discrete Uniform Distribution
4.4 The Hypergeometric Distribution
MINITAB
PRACTICE PROBLEMS FOR SECTIONS 4.3 AND 4.4
4.5 The Bernoulli Distribution
4.6 The Binomial Distribution
MINITAB
Proof
PRACTICE PROBLEMS FOR SECTIONS 4.5 AND 4.6
4.7 The Multinomial Distribution
PRACTICE PROBLEMS FOR SECTION 4.7
4.8 The Poisson Distribution. 4.8.1 Definition and Properties of the Poisson Distribution
4.8.2 Poisson Process
4.8.3 Poisson Distribution as a Limiting Form of the Binomial
MINITAB
PRACTICE PROBLEMS FOR SECTION 4.8
4.9 The Negative Binomial Distribution
MINITAB
PRACTICE PROBLEMS FOR SECTION 4.9
4.10 Some Derivations and Proofs (Optional)
4.11 A Case Study
4.12 Using JMP
Review Practice Problems
Note
Chapter 5 Continuous Random Variables and Some Important Continuous Probability Distributions
Topics Covered
Learning Outcomes
5.1 Continuous Random Variables
5.2 Mean and Variance of Continuous Random Variables. 5.2.1 Expected Value of Continuous Random Variables and Their Functions
Certain Properties of an Expected Value and Variance of a Random Variable
5.2.2 The Moment‐Generating Function and Expected Value of a Special Function of X
PRACTICE PROBLEMS FOR SECTIONS 5.1 AND 5.2
5.3 Chebyshev's Inequality
PRACTICE PROBLEMS FOR SECTION 5.3
5.4 The Uniform Distribution. 5.4.1 Definition and Properties
MINITAB
5.4.2 Mean and Standard Deviation of the Uniform Distribution
PRACTICE PROBLEMS FOR SECTION 5.4
5.5 The Normal Distribution. 5.5.1 Definition and Properties
5.5.2 The Standard Normal Distribution
MINITAB
5.5.3 The Moment‐Generating Function of the Normal Distribution
Theorem 5.5.1
PRACTICE PROBLEMS FOR SECTION 5.5
5.6 Distribution of Linear Combination of Independent Normal Variables
Theorem 5.6.1
Theorem 5.6.2
Theorem 5.6.3
Theorem 5.6.4
PRACTICE PROBLEMS FOR SECTION 5.6
5.7 Approximation of the Binomial and Poisson Distributions by the Normal Distribution. 5.7.1 Approximation of the Binomial Distribution by the Normal Distribution
5.7.2 Approximation of the Poisson Distribution by the Normal Distribution
5.8 A Test of Normality
MINITAB
PRACTICE PROBLEMS FOR SECTIONS 5.7 AND 5.8
5.9 Probability Models Commonly used in Reliability Theory
5.9.1 The Lognormal Distribution. Definition 5.9.1
PRACTICE PROBLEMS FOR SECTION 5.9.1
5.9.2 The Exponential Distribution
Definition 5.9.2
Distribution Function of the Exponential Distribution
PRACTICE PROBLEMS FOR SECTION 5.9.2
5.9.3 The Gamma Distribution
Definition 5.9.3
MINITAB
5.9.4 The Weibull Distribution
Definition 5.9.4
Mean and Variance of the Weibull Distribution
MINITAB
PRACTICE PROBLEMS FOR SECTIONS 5.9.3 AND 5.9.4
5.10 A Case Study
5.11 Using JMP
Review Practice Problems
Note
Chapter 6 Distribution of Functions of Random Variables
Topics Covered
Learning Outcomes
6.1 Introduction
6.2 Distribution Functions of Two Random Variables
6.2.1 Case of Two Discrete Random Variables
6.2.2 Case of Two Continuous Random Variables
6.2.3 The Mean Value and Variance of Functions of Two Random Variables
Proof
6.2.4 Conditional Distributions
6.2.5 Correlation between Two Random Variables
Solution:
6.2.6 Bivariate Normal Distribution
PRACTICE PROBLEMS FOR SECTION 6.2
6.3 Extension to Several Random Variables
6.4 The Moment‐Generating Function Revisited
PRACTICE PROBLEMS FOR SECTIONS 6.3 AND 6.4
Review Practice Problems
Chapter 7 Sampling Distributions
Topics Covered
Learning Outcomes
7.1 Random Sampling
7.1.1 Random Sampling from an Infinite Population
7.1.2 Random Sampling from a Finite Population
PRACTICE PROBLEMS FOR SECTION 7.1
7.2 The Sampling Distribution of the Sample Mean
7.2.1 Normal Sampled Population
7.2.2 Nonnormal Sampled Population
7.2.3 The Central Limit Theorem
PRACTICE PROBLEMS FOR SECTION 7.2
7.3 Sampling from a Normal Population
7.3.1 The Chi‐Square Distribution
Definition 7.3.1
Definition 7.3.2
7.3.2 The Student ‐Distribution
Definition 7.3.3
7.3.3 Snedecor's ‐Distribution
Definition 7.3.4
7.4 Order Statistics
7.4.1 Distribution of the Largest Element in a Sample
7.4.2 Distribution of the Smallest Element in a Sample
7.4.3 Distribution of the Median of a Sample and of the kth Order Statistic
7.4.4 Other Uses of Order Statistics. The Range as an Estimate of in Normal Samples
PRACTICE PROBLEMS FOR SECTION 7.4
7.5 Using JMP
Review Practice Problems
Chapter 8 Estimation of Population Parameters
Topics Covered
Learning Outcomes
8.1 Introduction
8.2 Point Estimators for the Population Mean and Variance
8.2.1 Properties of Point Estimators
Definition 8.2.1
Definition 8.2.2
Definition 8.2.3
Definition 8.2.4
8.2.2 Methods of Finding Point Estimators
Method of Moments
Method of Maximum Likelihood
Definition 8.2.5
PRACTICE PROBLEMS FOR SECTION 8.2
8.3 Interval Estimators for the Mean of a Normal Population. 8.3.1 Known
Definition 8.3.1
Theorem 8.3.1
8.3.2 Unknown
Theorem 8.3.2
8.3.3 Sample Size Is Large
One‐Sided Confidence Interval
PRACTICE PROBLEMS FOR SECTION 8.3
8.4 Interval Estimators for The Difference of Means of Two Normal Populations. 8.4.1 Variances Are Known
Theorem 8.4.1
8.4.2 Variances Are Unknown
PRACTICE PROBLEMS FOR SECTION 8.4
8.5 Interval Estimators for the Variance of a Normal Population
Theorem 8.5.1
8.6 Interval Estimator for the Ratio of Variances of Two Normal Populations
Theorem 8.6.1
PRACTICE PROBLEMS FOR SECTIONS 8.5 AND 8.6
8.7 Point and Interval Estimators for the Parameters of Binomial Populations. 8.7.1 One Binomial Population
8.7.2 Two Binomial Populations
PRACTICE PROBLEMS FOR SECTION 8.7
8.8 Determination of Sample Size
8.8.1 One Population Mean
8.8.2 Difference of Two Population Means
8.8.3 One Population Proportion
8.8.4 Difference of Two Population Proportions
PRACTICE PROBLEMS FOR SECTION 8.8
8.9 Some Supplemental Information
8.10 A Case Study
8.11 Using JMP
Review Practice Problems
Note
Chapter 9 Hypothesis Testing
Topics Covered
Learning Outcomes
9.1 Introduction
9.2 Basic Concepts of Testing a Statistical Hypothesis. 9.2.1 Hypothesis Formulation
9.2.2 Risk Assessment
PRACTICE PROBLEMS FOR SECTION 9.2
9.3 Tests Concerning the Mean of a Normal Population Having Known Variance. 9.3.1 Case of a One‐Tail (Left‐Sided) Test
Definition 9.3.1
9.3.2 Case of a One‐Tail (Right‐Sided) Test
9.3.3 Case of a Two‐Tail Test
Solution:
Solution:
PRACTICE PROBLEMS FOR SECTION 9.3
9.4 Tests Concerning the Mean of a Normal Population Having Unknown Variance
9.4.1 Case of a Left‐Tail Test
Solution:
9.4.2 Case of a Right‐Tail Test
9.4.3 The Two‐Tail Case
PRACTICE PROBLEMS FOR SECTION 9.4
9.5 Large Sample Theory
Solution:
PRACTICE PROBLEMS FOR SECTION 9.5
9.6 Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances. 9.6.1 The Left‐Tail Test
9.6.2 The Right‐Tail Test
Solution:
9.6.3 The Two‐Tail Test
Solution:
PRACTICE PROBLEMS FOR SECTION 9.6
9.7 Tests Concerning the Difference of Means of Two Populations Having Normal Distributions with Unknown Variances
9.7.1 Two Population Variances are Equal
The Left‐Tail Test
The Right‐Tail Test
The Two‐Tail Test
Solution:
9.7.2 Two Population Variances are Unequal
Solution:
9.7.3 The Paired ‐Test
PRACTICE PROBLEMS FOR SECTION 9.7
9.8 Testing Population Proportions
9.8.1 Test Concerning One Population Proportion
Solution:
9.8.2 Test Concerning the Difference Between Two Population Proportions
Solution:
PRACTICE PROBLEMS FOR SECTION 9.8
9.9 Tests Concerning the Variance of a Normal Population
PRACTICE PROBLEMS FOR SECTION 9.9
9.10 Tests Concerning the Ratio of Variances of Two Normal Populations
PRACTICE PROBLEMS FOR SECTION 9.10
9.11 Testing of Statistical Hypotheses using Confidence Intervals
Solution:
Solution:
9.12 Sequential Tests of Hypotheses. 9.12.1 A One‐Tail Sequential Testing Procedure
Solution:
9.12.2 A Two‐Tail Sequential Testing Procedure
PRACTICE PROBLEMS FOR SECTIONS 9.11 AND 9.12
9.13 Case Studies
9.14 Using JMP
Review Practice Problems
Chapter 10 Elements of Reliability Theory
Topics Covered
Learning Outcomes
10.1 The Reliability Function
10.1.1 The Hazard Rate Function
Solution:
Solution:
10.1.2 Employing the Hazard Function
PRACTICE PROBLEMS FOR SECTION 10.1
10.2 Estimation: Exponential Distribution
PRACTICE PROBLEMS FOR SECTION 10.2
10.3 Hypothesis Testing: Exponential Distribution
Solution:
10.4 Estimation: Weibull Distribution
Solution:
PRACTICE PROBLEMS FOR SECTIONS 10.3 AND 10.4
10.5 Case Studies
10.6 Using JMP
Review Practice Problems
Notes
Chapter 11 On Data Mining
Topics Covered
Learning Outcomes
11.1 Introduction
11.2 What is Data Mining?
11.2.1 Big Data
11.3 Data Reduction
11.4 Data Visualization
Solution:
11.5 Data Preparation
11.5.1 Missing Data
11.5.2 Outlier Detection and Remedial Measures
Solution:
11.6 Classification
11.6.1 Evaluating a Classification Model
11.7 Decision Trees
11.7.1 Classification and Regression Trees (CART)
Classification Trees
Definition Definition 11.7.1
Regression Trees
11.7.2 Further Reading
11.8 Case Studies
11.9 Using JMP
Review Practice Problems
Notes
Chapter 12 Cluster Analysis
Topics Covered
Learning Outcomes
12.1 Introduction
12.2 Similarity Measures
12.2.1 Common Similarity Coefficients
Solution:
12.3 Hierarchical Clustering Methods
12.3.1 Single Linkage
Solution
MINITAB
12.3.2 Complete Linkage
12.3.3 Average Linkage
Solution:
MINITAB
12.3.4 Ward's Hierarchical Clustering
Solution:
12.4 Nonhierarchical Clustering Methods
12.4.1 ‐Means Method
Solution:
MINITAB
12.5 Density‐Based Clustering
12.6 Model‐Based Clustering
12.7 A Case Study
12.8 Using JMP
Review Practice Problems
Notes
Chapter 13 Analysis of Categorical Data
Topics Covered
Learning Outcomes
13.1 Introduction
13.2 The Chi‐Square Goodness‐of‐Fit Test
Solution:
Solution:
Solution:
Solution:
PRACTICE PROBLEMS FOR SECTION 13.2
13.3 Contingency Tables
13.3.1 The Case with Known Parameters
Theorem 13.3.1
Solution:
13.3.2 The Case with Unknown Parameters
Solution:
13.3.3 The Contingency Table
Theorem 13.3.2
Solution:
Solution:
PRACTICE PROBLEMS FOR SECTION 13.3
13.4 Chi‐Square Test for Homogeneity
Solution:
PRACTICE PROBLEMS FOR SECTION 13.4
13.5 Comments on the Distribution of the Lack‐of‐Fit Statistics
13.6 Case Studies
Using JMP
Review Practice Problems
Note
Chapter 14 Nonparametric Tests
Topics Covered
Learning Outcomes
14.1 Introduction
14.2 The Sign Test
14.2.1 One‐Sample Test
Solution:
14.2.2 The Wilcoxon Signed‐Rank Test
Solution:
14.2.3 Two‐Sample Test
Solution:
Solution:
PRACTICE PROBLEMS FOR SECTION 14.2
14.3 Mann–Whitney (Wilcoxon) Test for Two Samples
Solution:
PRACTICE PROBLEMS FOR SECTION 14.3
14.4 Runs Test
14.4.1 Runs above and below the Median
Solution:
14.4.2 The Wald–Wolfowitz Run Test
Solution:
PRACTICE PROBLEMS FOR SECTION 14.4
14.5 Spearman Rank Correlation
Solution:
PRACTICE PROBLEMS FOR SECTION 14.5
14.6 Using JMP
Review Practice Problems
Chapter 15 Simple Linear Regression Analysis
Topics Covered
Learning Outcomes
15.1 Introduction
15.2 Fitting the Simple Linear Regression Model
15.2.1 Simple Linear Regression Model
15.2.2 Fitting a Straight Line by Least Squares
Solution:
15.2.3 Sampling Distribution of the Estimators of Regression Coefficients
PRACTICE PROBLEMS FOR SECTION 15.2
15.3 Unbiased Estimator of σ2
Solution:
15.4 Further Inferences Concerning Regression Coefficients (, ),, and. 15.4.1 Confidence Interval for with Confidence Coefficient
Solution:
15.4.2 Confidence Interval for with Confidence Coefficient
Solution:
15.4.3 Confidence Interval for with Confidence Coefficient (1 )
Solution:
15.4.4 Prediction Interval for a Future Observation Y with Confidence Coefficient ()
Solution:
15.5 Tests of Hypotheses for and
15.5.1 Test of Hypotheses for
15.5.2 Test of Hypotheses for
Solution:
Solution:
Solution:
15.6 Analysis of Variance Approach to Simple Linear Regression Analysis
Solution:
Solution: MINITAB
15.7 Residual Analysis
Solution:
15.8 Transformations
Definition 15.8.1
Solution:
15.9 Inference About ρ
15.10 A Case Study
15.11 Using JMP
Review Practice Problems
Note
Chapter 16 Multiple Linear Regression Analysis
Topics Covered
Learning Outcomes
16.1 Introduction
16.2 Multiple Linear Regression Models
Solution:
16.3 Estimation of Regression Coefficients
Solution:
16.3.1 Estimation of Regression Coefficients Using Matrix Notation
Solution:
16.3.2 Properties of the Least‐Squares Estimators
16.3.3 The Analysis of Variance Table
16.3.4 More Inferences about Regression Coefficients
Test and Confidence Interval for an Individual Regression Parameter ,
Tests and Confidence Intervals for Subsets of ) Regression Coefficients
Solution:
Tests for Subsets of () Regression Parameters Using Matrix Notation
Confidence Interval for an Expected Response at with Confidence Coefficient
Prediction Interval for a Future Response at with Confidence Coefficient
Solution:
PRACTICE PROBLEMS FOR SECTION 16.3
16.4 Multiple Linear Regression Model Using Quantitative and Qualitative Predictor Variables
16.4.1 Single Qualitative Variable with Two Categories
16.4.2 Single Qualitative Variable with Three or More Categories
Solution:
Solution:
PRACTICE PROBLEMS FOR SECTION 16.4
16.5 Standardized Regression Coefficients
16.5.1 Multicollinearity
16.5.2 Consequences of Multicollinearity
16.6 Building Regression Type Prediction Models
16.6.1 First Variable to Enter into the Model
Solution:
16.7 Residual Analysis and Certain Criteria for Model Selection. 16.7.1 Residual Analysis
16.7.2 Certain Criteria for Model Selection
Coefficient of Multiple Determination—R
Adjusted Coefficient of Multiple Determination—
Mallows' Statistic
PRESS Statistic
PRACTICE PROBLEMS FOR SECTIONS 16.6 AND 16.7
16.8 Logistic Regression
Solution:
PRACTICE PROBLEMS FOR SECTION 16.8
16.9 Case Studies
16.10 Using JMP
Review Practice Problems
Notes
Chapter 17 Analysis of Variance
Topics Covered
Learning Outcomes
17.1 Introduction
17.2 The Design Models
17.2.1 Estimable Parameters
17.2.2 Estimable Functions
PRACTICE PROBLEMS FOR SECTION 17.2
17.3 One‐Way Experimental Layouts. 17.3.1 The Model and Its Analysis
17.3.2 Confidence Intervals for Treatment Means
Solution:
Solution:
17.3.3 Multiple Comparisons
Definition 17.3.1
Definition 17.3.2
Definition 17.3.3
Definition 17.3.4
Definition 17.3.5
Solution:
17.3.4 Determination of Sample Size
17.3.5 The Kruskal–Wallis Test for One‐Way Layouts (Nonparametric Method)
Solution:
PRACTICE PROBLEMS FOR SECTION 17.3
17.4 Randomized Complete Block (RCB) Designs
Solution:
17.4.1 The Friedman ‐Test for Randomized Complete Block Design (Nonparametric Method)
Solution:
17.4.2 Experiments with One Missing Observation in an RCB‐Design Experiment
17.4.3 Experiments with Several Missing Observations in an RCB‐Design Experiment
PRACTICE PROBLEMS FOR SECTION 17.4
17.5 Two‐Way Experimental Layouts
17.5.1 Two‐Way Experimental Layouts with One Observation per Cell
17.5.2 Two‐Way Experimental Layouts with Observations per Cell
Solution:
Solution:
Solution:
17.5.3 Blocking in Two‐Way Experimental Layouts
17.5.4 Extending Two‐Way Experimental Designs to ‐Way Experimental Layouts
PRACTICE PROBLEMS FOR SECTION 17.5
17.6 Latin Square Designs
Solution:
PRACTICE PROBLEMS FOR SECTION 17.6
17.7 Random‐Effects and Mixed‐Effects Models. 17.7.1 Random‐Effects Model
17.7.2 Mixed‐Effects Model
17.7.3 Nested (Hierarchical) Designs
Solution:
PRACTICE PROBLEMS FOR SECTION 17.7
17.8 A Case Study
17.9 Using JMP
Review Practice Problems
Note
Chapter 18 The 2k Factorial Designs
Topics Covered
Learning Outcomes
18.1 Introduction
18.2 The Factorial Designs
PRACTICE PROBLEMS FOR SECTION 18.2
18.3 The 2 Factorial Designs
Solution:
PRACTICE PROBLEMS FOR SECTION 18.3
18.4 Unreplicated 2 Factorial Designs
PRACTICE PROBLEMS FOR SECTION 18.4
18.5 Blocking in the 2 Factorial Design
18.5.1 Confounding in the Factorial Design
Solution:
Solution:
18.5.2 Yates's Algorithm for the Factorial Designs
PRACTICE PROBLEMS FOR SECTION 18.5
18.6 The 2k Fractional Factorial Designs
18.6.1 One‐half Replicate of a Factorial Design
Solution:
18.6.2 One‐quarter Replicate of a Factorial Design
Solution:
PRACTICE PROBLEMS FOR SECTION 18.6
18.7 Case Studies
18.8 Using JMP
Review Practice Problems
Notes
Chapter 19 Response Surfaces
Topics Covered
Learning Outcomes
19.1 Introduction
19.1.1 Basic Concepts of Response Surface Methodology
19.2 First‐Order Designs
MINITAB
Solution:
PRACTICE PROBLEM FOR SECTION 19.2
19.3 Second‐Order Designs
19.3.1 Central Composite Designs (CCDs)
Definition 19.3.1
Solution:
19.3.2 Some Other First‐Order and Second‐Order Designs
Solution:
PRACTICE PROBLEM FOR SECTION 19.3
19.4 Determination of Optimum or Near‐Optimum Point
19.4.1 The Method of Steepest Ascent
19.4.2 Analysis of a Fitted Second‐Order Response Surface
Solution:
PRACTICE PROBLEMS FOR SECTION 19.4
19.5 Anova Table for a Second‐Order Model
19.6 Case Studies
19.7 Using JMP
Review Practice Problems
Note
Chapter 20 Statistical Quality Control—Phase I Control Charts
Topics Covered
Learning Outcomes
Chapter 21 Statistical Quality Control—Phase II Control Charts
Topics Covered
Learning Outcomes
Appendices
Note
A. Statistical Tables
B Answers to Selected Problems. CHAPTER 2. Sections 2.1 and 2.2
Section 2.3
Section 2.4
Section 2.5
Section 2.6
Sections 2.7 and 2.8
Section 2.9
Review Practice Problems
CHAPTER 3. Sections 3.2 and 3.3
Section 3.4
Sections 3.5 and 3.6
Review Practice Problems
CHAPTER 4. Sections 4.1 and 4.2
Sections 4.3 and 4.4
Sections 4.5 and 4.6
Section 4.7
Section 4.8
Section 4.9
Review Practice Problems
CHAPTER 5. Sections 5.1 and 5.2
Section 5.3
Section 5.4
Section 5.5
Section 5.6
Sections 5.7 and 5.8
Section 5.9.1
Section 5.9.2
Sections 5.9.3 and 5.9.4
Review Practice Problems
CHAPTER 6. Section 6.2
Sections 6.3 and 6.4
Review Practice Problems
CHAPTER 7. Section 7.1
Section 7.2
Section 7.3
Section 7.4
Review Practice Problems
CHAPTER 8. Section 8.2
Section 8.3
Section 8.4
Sections 8.5 and 8.6
Section 8.7
Section 8.8
Review Practice Problems
CHAPTER 9. Section 9.2
Section 9.3
Section 9.4
Section 9.5
Section 9.6
Section 9.7
Section 9.8
Section 9.9
Section 9.10
Sections 9.11 and 9.12
Review Practice Problems
CHAPTER 10. Section 10.1
Section 10.2
Sections 10.3 and 10.4
Review Practice Problems
CHAPTER 11. Review Practice Problems
CHAPTER 12. Review Practice Problems
CHAPTER 13. Section 13.2
Section 13.3
Section 13.4
Review Practice Problems
CHAPTER 14. Section 14.2
Section 14.3
Section 14.4
Section 14.5
Review Practice Problems
CHAPTER 15. Section 15.2
Sections 15.3 and 15.4
Section 15.5
Section 15.6
Section 15.7
Section 15.8
Section 15.9
Review Practice Problems
CHAPTER 16. Section 16.3
Section 16.4
Sections 16.6 and 16.7
Section 16.8
Review Practice Problems
CHAPTER 17. Section 17.2
Section 17.3
Section 17.4
Section 17.5
Section 17.6
Section 17.7
Review Practice Problems
CHAPTER 18. Section 18.2
Section 18.3
Section 18.4
Section 18.5
Section 18.6
Review Practice Problems
CHAPTER 19. Section 19.2
Section 19.3
Section 19.4
Review Practice Problems
CHAPTER 20. Section 20.3 and 20.4
Section 20.5
Section 20.6
Section 20.7
Review Practice Problems
CHAPTER 21. Section 21.2
Section 21.3
Section 21.4
Section 21.5
Review Practice Problems
C Bibliography
Index
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