Table of contents for Miller and Freund's probability and statistics for engineers / Richard A. Johnson.


Bibliographic record and links to related information available from the Library of Congress catalog. Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.


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Contents
1 Introduction 7 1.1 Why Study Statistics? 	7
1.2 Modern Statistics 	8
1.3 Statistics and Engineering 	9
1.4 The Role of the Scientist and Engineer in Quality Improvement 	9 1.5 A Case Study : Visually Inspecting Data to Improve Product Quality . . . 10
1.6 Two Basic Concepts - Population and Sample 	12
2 Treatment of Data 21 2.1 Pareto Diagrams and Dot Diagrams 	21
2.2 Frequency Distributions 	24
2.3 Graphs of Frequency Distributions 	28
2.4 Stem-and-leaf Displays 	33
2.5 Descriptive Measures 	41
2.6 Quartiles and Percentiles 	47
2.7 The Calculation of x and s 	53
2.8 A Case Study: Problems with Aggregating Data 	63
3 Probability 73 3.1 Sample Spaces and Events 	73
3.2 Counting 	79
3.3 Probability 	89
3.4 The Axioms of Probability 	91
3.5 Some Elementary Theorems 	94
3.6 Conditional Probability 	107
3.7 Bayes? Theorem 	113
3.8 Mathematical Expectation and Decision Making 	123
4 Probability Distributions 135 4.1 Random Variables 	135
4.2 The Binomial Distribution 	140
4.3 The Hypergeometric Distribution 	146
4.4 The Mean and the Variance of a Probability Distribution 	154
4.5 Chebyshev?s Theorem 	162
4.6 The Poisson Approximation to the Binomial Distribution 	168
4.7 Poisson Processes 	172
4.8 The Geometric Distribution 	175
4.9 The Multinomial Distribution 	180
4.10 Simulation 	182
5 Probability Densities 193 5.1 Continuous Random Variables 	193
5.2 The Normal Distribution 	202
5.3 The Normal Approximation to the Binomial Distribution 	214
5.4 Other Probability Densities 	222
5.5 The Uniform Distribution 	222
5.6 The Log-Normal Distribution 	224
5.7 The Gamma Distribution 	228
5.8 The Beta Distribution 	232
5.9 The Weibull Distribution 	234
5.10 Joint Distributions - Discrete and Continuous 	239
5.11 Checking if the Data Are Normal 	256
5.12 Transforming Observations to Near Normality 	259
5.13 Simulation 	261
6 Sampling Distributions 275 6.1 Populations and Samples 	275
6.2 The Sampling Distribution of the Mean (
 known) 	280
6.3 The Sampling Distribution of the Mean (
 unknown) 	292
6.4 The Sampling Distribution of the variance 	294
7 Inferences Concerning Means 305 7.1 Point Estimation 	305
7.2 Interval Estimation 	312
7.3 Tests of Hypotheses 	321
7.4 Null Hypotheses and Tests of Hypotheses 	324
7.5 Hypotheses Concerning One Mean 	331
7.6 The Relation Between Tests and Confidence Intervals 	339
7.7 Operating Characteristic Curves 	340
7.8 Inference Concerning Two Means 	350
7.9 Design Issues?Randomization and Pairing 	367
8 Inferences Concerning Variances 377 8.1 The Estimation of Variances 	377
8.2 Hypotheses Concerning One Variance 	380
8.3 Hypotheses Concerning Two Variances 	382
9 Inferences Concerning Proportions 391 9.1 Estimation of Proportions 	391
9.2 Hypotheses Concerning One Proportion 	399
9.3 Hypotheses Concerning Several Proportions 	401
9.4 Analysis of r ? c Tables 	411
9.5 Goodness of Fit 	414
10 Nonparametric Tests 425 10.1 Introduction 	425
10.2 The Sign Test 	426
10.3 Rank-Sum Tests 	428
10.4 Tests of Randomness 	436 
10.5 The Kolmogorov-Smirnov and Anderson
Darling Tests 	439
11 Curve Fitting 447 11.1 The Method of Least Squares 	447
11.2 Inferences Based on the Least-Squares Estimators 	455
11.3 Curvilinear Regression 	472
11.4 Multiple Regression 	479
11.5 Checking the Adequacy of the Model 	485
11.6 Correlation 	494
11.7 Multiple Linear Regression?(Matrix Notation) 	511
12 Analysis of Variance 523 12.1 Some General Principles 	523
12.2 Completely Randomized Designs 	527
12.3 Randomized-Block Designs 	546
12.4 Multiple Comparisons 	555
12.5 Some Further Experimental Designs 	562
12.6 Analysis of Covariance 	572
13 Factorial Experimentation 587 13.1 Two-Factor Experiments 	587
13.2 Multifactor Experiments 	597
13.3 2n Factorial Experiments 	614
13.4 The Graphic Presentation of 22 and 23 Experiments 	624
13.5 Confounding in a 2n Factorial Experiment 	646
13.6 Fractional Replication 	651
14 The Statistical Content of Quality-Improvement Programs 667 14.1 Quality-
Improvement Programs 	667
14.2 Starting a Quality-Improvement Program 	671
14.3 Experimental Designs for Quality 	673
14.4 Quality Control 	678
14.5 Control Charts for Measurements 	680
14.6 Control Charts for Attributes 	686
14.7 Tolerance Limits 	695
14.8 Acceptance Sampling 	698
15 Application to Reliability and Life Testing 715 15.1 Reliability 	715
15.2 Failure-Time Distribution 	719
15.3 The Exponential Model in Reliability 	722
15.4 The Exponential Model in Life Testing 	727
15.5 The Weibull Model in Life Testing 	732




Library of Congress Subject Headings for this publication: Engineering Statistical methods, Probabilities