# Miller and Freund's probability and statistics for engineers /

##### By: Johnson, Richard Arnold

##### Title By: Miller, Irwin | Freund, John

Material type: BookPublisher: Harlow : Pearson, c2014.Edition: 8th ed.Description: iv, 770 p. : ill. ; 28 cm.ISBN: 9781292023830Program: STAT291Other title: Probability and statistics for engineers.Subject(s): Engineering -- Statistical methods | ProbabilitiesDDC classification: 519.202/462Item type | Home library | Call number | Status | Date due | Barcode | Item holds | Course reserves |
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REGULAR | University of Wollongong in Dubai Main Collection | 519.202462 JO MI (Browse shelf) | Available | T0016101 | |||

REGULAR | University of Wollongong in Dubai Main Collection | 519.202462 JO MI (Browse shelf) | Available | T0016102 | |||

REGULAR | University of Wollongong in Dubai Main Collection | 519.202462 JO MI (Browse shelf) | Available | T0016103 |

Preface 1. Introduction 1.1 Why Study Statistics? 1.2 Modern Statistics 1.3 Statistics and Engineering 1.4 The Role of the Scientist and Engineer in Quality Improvement 1.5 A Case Study: Visually Inspecting Data to Improve Product Quality 1.6 Two Basic Concepts-Population and Sample 2. Organization and Description of Data 2.1 Pareto Diagrams and Dot Diagrams 2.2 Frequency Distributions 2.3 Graphs of Frequency Distributions 2.4 Stem-and-Leaf Displays 2.5 Descriptive Measures 2.6 Quartiles and Percentiles 2.7 The Calculation of x and s 2.8 A Case Study: Problems with Aggregating Data 3. Probability 3.1 Sample Spaces and Events 3.2 Counting 3.3 Probability 3.4 The Axioms of Probability 3.5 Some Elementary Theorems 3.6 Conditional Probability 3.7 Bayes' Theorem 4. Probability Distributions 4.1 Random Variables 4.2 The Binomial Distribution 4.3 The Hypergeometric Distribution 4.4 The Mean and the Variance of a Probability Distribution 4.5 Chebyshev's Theorem 4.6 The Poisson Approximation to the Binomial Distribution 4.7 Poisson Processes 4.8 The Geometric and Negative Binomial Distribution 4.9 The Multinomial Distribution 4.10 Simulation 5. Probability Densities 5.1 Continuous Random Variables 5.2 The Normal Distribution 5.3 The Normal Approximation to the Binomial Distribution 5.4 Other Probability Densities 5.5 The Uniform Distribution 5.6 The Log-Normal Distribution 5.7 The Gamma Distribution 5.8 The Beta Distribution 5.9 The Weibull Distribution 5.10 Joint Distributions-Discrete and Continuous 5.11 Moment Generating Functions 5.12 Checking If the Data Are Normal 5.13 Transforming Observations to Near Normality 5.14 Simulation 6. Sampling Distributions 6.1 Populations and Samples 6.2 The Sampling Distribution of the Mean (s known) 6.3 The Sampling Distribution of the Mean (s unknown) 6.4 The Sampling Distribution of the Variance 6.5 Representations of the Normal Theory Distributions 6.6 The Moment Generating Function Method to Obtain Distributions 6.7 Transformation Methods to Obtain Distributions 7. Inferences Concerning a Mean 7.1 Point Estimation 7.2 Interval Estimation 7.3 Maximum Likelihood Estimation 7.4 Tests of Hypotheses 7.5 Null Hypotheses and Tests of Hypotheses 7.6 Hypotheses Concerning One Mean 7.7 The Relation between Tests and Confidence Intervals 7.8 Power, Sample Size, and Operating Characteristic Curves 8. Comparing Two Treatments 8.1 Experimental Designs for Comparing Two Treatments 8.2 Comparisons-Two Independent Large Samples 8.3 Comparisons-Two Independent Small Samples 8.4 Matched Pairs Comparisons 8.5 Design Issues-Randomization and Pairing 9. Inferences Concerning Variances 9.1 The Estimation of Variances 9.2 Hypotheses Concerning One Variance 9.3 Hypotheses Concerning Two Variances 10. Inferences Concerning Proportions 10.1 Estimation of Proportions 10.2 Hypotheses Concerning One Proportion 10.3 Hypotheses Concerning Several Proportions 10.4 Analysis of r x c Tables 10.5 Goodness of Fit 11. Regression Analysis 11.1 The Method of Least Squares 11.2 Inferences Based on the Least Squares Estimators 11.3 Curvilinear Regression 11.4 Multiple Regression 11.5 Checking the Adequacy of the Model 11.6 Correlation 11.7 Multiple Linear Regression (Matrix Notation) 12. Analysis of Variance 12.1 Some General Principles 12.2 Completely Randomized Designs 12.3 Randomized-Block Designs 12.4 Multiple Comparisons 12.5 Analysis of Covariance 13. Factorial Experimentation 13.1 Two-Factor Experiments 13.2 Multifactor Experiments 13.3 2n Factorial Experiments 13.4 The Graphic Presentation of 22 and 23 Experiments 13.5 Response Surface Analysis 13.6 Confounding in a 2n Factorial Experiment 13.7 Fractional Replication 14. Nonparametric Tests 14.1 Introduction 14.2 The Sign Test 14.3 Rank-Sum Tests 14.4 Correlation Based on Ranks 14.5 Tests of Randomness 14.6 The Kolmogorov-Smirnov and Anderson-Darling Tests 15. The Statistical Content of Quality-Improvement Programs 15.1 Quality-Improvement Programs 15.2 Starting a Quality-Improvement Program 15.3 Experimental Designs for Quality 15.4 Quality Control 15.5 Control Charts for Measurements 15.6 Control Charts for Attributes 15.7 Tolerance Limits 16. Application to Reliability and Life Testing 16.1 Reliability 16.2 Failure-Time Distribution 16.3 The Exponential Model in Life Testing 16.4 The Weibull Model in Life Testing Appendix D Answers to Odd-Numbered Exercises.

STAT291

For an introductory, one or two semester, sophomore-junior level course in Probability and Statistics or Applied Statistics for engineering, physical science, and mathematics students. This text is rich in exercises and examples, and explores both elementary probability and basic statistics, with an emphasis on engineering and science applications. Much of the data have been collected from the author's own consulting experience and from discussions with scientists and engineers about the use of statistics in their fields. In later chapters, the text emphasizes designed experiments, especially two-level factorial design.