Introduction to Probability and Statistics for Engineers and Scientists, 6th Edition
- Length: 704 pages
- Edition: 6
- Language: English
- Publisher: Academic Press
- Publication Date: 2020-11-30
- ISBN-10: 0128243465
- ISBN-13: 9780128243466
- Sales Rank: #448725 (See Top 100 Books)
Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and scientists. Utilizing real data from actual studies across life science, engineering, computing and business, this useful introduction supports reader comprehension through a wide variety of exercises and examples. End-of-chapter reviews of materials highlight key ideas, also discussing the risks associated with the practical application of each material. In the new edition, coverage includes information on Big Data and the use of R.
This book is intended for upper level undergraduate and graduate students taking a probability and statistics course in engineering programs as well as those across the biological, physical and computer science departments. It is also appropriate for scientists, engineers and other professionals seeking a reference of foundational content and application to these fields.
Front Cover Introduction to Probability and Statistics for Engineers and Scientists Copyright Contents Preface Organization and coverage Preface Acknowledgments 1 Introduction to statistics 1.1 Introduction 1.2 Data collection and descriptive statistics 1.3 Inferential statistics and probability models 1.4 Populations and samples 1.5 A brief history of statistics Problems 2 Descriptive statistics 2.1 Introduction 2.2 Describing data sets 2.2.1 Frequency tables and graphs 2.2.2 Relative frequency tables and graphs 2.2.3 Grouped data, histograms, ogives, and stem and leaf plots 2.3 Summarizing data sets 2.3.1 Sample mean, sample median, and sample mode Germ-Free Mice Conventional Mice 2.3.2 Sample variance and sample standard deviation An algebraic identity 2.3.3 Sample percentiles and box plots 2.4 Chebyshev's inequality Chebyshev's inequality The one-sided Chebyshev inequality 2.5 Normal data sets The empirical rule 2.6 Paired data sets and the sample correlation coefficient Properties of r 2.7 The Lorenz curve and Gini index 2.8 Using R Problems 3 Elements of probability 3.1 Introduction 3.2 Sample space and events 3.3 Venn diagrams and the algebra of events 3.4 Axioms of probability 3.5 Sample spaces having equally likely outcomes Basic principle of counting Proof of the Basic Principle Notation and terminology 3.6 Conditional probability 3.7 Bayes' formula 3.8 Independent events Problems 4 Random variables and expectation 4.1 Random variables 4.2 Types of random variables 4.3 Jointly distributed random variables 4.3.1 Independent random variables 4.3.2 Conditional distributions 4.4 Expectation Remarks 4.5 Properties of the expected value 4.5.1 Expected value of sums of random variables 4.6 Variance Remark Remark 4.7 Covariance and variance of sums of random variables 4.8 Moment generating functions 4.9 Chebyshev's inequality and the weak law of large numbers Problems 5 Special random variables 5.1 The Bernoulli and binomial random variables 5.1.1 Using R to calculate binomial probabilities 5.2 The Poisson random variable 5.2.1 Using R to calculate Poisson probabilities 5.3 The hypergeometric random variable 5.4 The uniform random variable 5.5 Normal random variables 5.6 Exponential random variables 5.6.1 The Poisson process 5.6.2 The Pareto distribution 5.7 The gamma distribution 5.8 Distributions arising from the normal 5.8.1 The chi-square distribution 5.8.1.1 The relation between chi-square and gamma random variables 5.8.2 The t-distribution 5.8.3 The F-distribution 5.9 The logistics distribution 5.10 Distributions in R Problems 6 Distributions of sampling statistics 6.1 Introduction 6.2 The sample mean 6.3 The central limit theorem 6.3.1 Approximate distribution of the sample mean 6.3.2 How large a sample is needed? 6.4 The sample variance 6.5 Sampling distributions from a normal population 6.5.1 Distribution of the sample mean 6.5.2 Joint distribution of X and S2 6.6 Sampling from a finite population Remark Problems 7 Parameter estimation 7.1 Introduction 7.2 Maximum likelihood estimators 7.2.1 Estimating life distributions 7.3 Interval estimates Remark 7.3.1 Confidence interval for a normal mean when the variance is unknown Remarks 7.3.2 Prediction intervals 7.3.3 Confidence intervals for the variance of a normal distribution 7.4 Estimating the difference in means of two normal populations Remark 7.5 Approximate confidence interval for the mean of a Bernoulli random variable Remark 7.6 Confidence interval of the mean of the exponential distribution 7.7 Evaluating a point estimator 7.8 The Bayes estimator Remark Remark: On choosing a normal prior Problems 8 Hypothesis testing 8.1 Introduction 8.2 Significance levels 8.3 Tests concerning the mean of a normal population 8.3.1 Case of known variance Remark 8.3.1.1 One-sided tests Remark Remarks 8.3.2 Case of unknown variance: the t-test 8.4 Testing the equality of means of two normal populations 8.4.1 Case of known variances 8.4.2 Case of unknown variances 8.4.3 Case of unknown and unequal variances 8.4.4 The paired t-test 8.5 Hypothesis tests concerning the variance of a normal population 8.5.1 Testing for the equality of variances of two normal populations 8.6 Hypothesis tests in Bernoulli populations 8.6.1 Testing the equality of parameters in two Bernoulli populations 8.7 Tests concerning the mean of a Poisson distribution 8.7.1 Testing the relationship between two Poisson parameters Problems 9 Regression 9.1 Introduction 9.2 Least squares estimators of the regression parameters 9.3 Distribution of the estimators Remarks Notation Computational identity for SSR 9.4 Statistical inferences about the regression parameters 9.4.1 Inferences concerning β Hypothesis test of H0: β= Confidence interval for β Remark 9.4.1.1 Regression to the mean 9.4.2 Inferences concerning α Confidence interval estimator of α 9.4.3 Inferences concerning the mean response α+βx0 Confidence interval estimator of α+βx0 9.4.4 Prediction interval of a future response Prediction interval for a response at the input level x0 Remarks 9.4.5 Summary of distributional results 9.5 The coefficient of determination and the sample correlation coefficient 9.6 Analysis of residuals: assessing the model 9.7 Transforming to linearity 9.8 Weighted least squares Remarks Remarks 9.9 Polynomial regression Remark 9.10 Multiple linear regression Remark Remark 9.10.1 Predicting future responses Confidence interval estimate of E [ Y|x] =∑ ki=0xiβi, (x 0≡ 1) Prediction Interval for Y(x) 9.10.2 Dummy variables for categorical data 9.11 Logistic regression models for binary output data Problems 10 Analysis of variance 10.1 Introduction 10.2 An overview 10.3 One-way analysis of variance The sum of squares identity 10.3.1 Using R to do the computations 10.3.2 Multiple comparisons of sample means 10.3.3 One-way analysis of variance with unequal sample sizes Remark 10.4 Two-factor analysis of variance: introduction and parameter estimation 10.5 Two-factor analysis of variance: testing hypotheses 10.6 Two-way analysis of variance with interaction Problems 11 Goodness of fit tests and categorical data analysis 11.1 Introduction 11.2 Goodness of fit tests when all parameters are specified Remarks 11.2.1 Determining the critical region by simulation Remarks 11.3 Goodness of fit tests when some parameters are unspecified 11.4 Tests of independence in contingency tables 11.5 Tests of independence in contingency tables having fixed marginal totals 11.6 The Kolmogorov-Smirnov goodness of fit test for continuous data Problems 12 Nonparametric hypothesis tests 12.1 Introduction 12.2 The sign test 12.3 The signed rank test 12.4 The two-sample problem 12.4.1 Testing the equality of multiple probability distributions 12.5 The runs test for randomness Problems 13 Quality control 13.1 Introduction 13.2 Control charts for average values: the x control chart Remarks 13.2.1 Case of unknown μ and σ Technical remark Remarks 13.3 S-control charts 13.4 Control charts for the fraction defective Remark 13.5 Control charts for number of defects 13.6 Other control charts for detecting changes in the population mean 13.6.1 Moving-average control charts 13.6.2 Exponentially weighted moving-average control charts 13.6.3 Cumulative sum control charts Problems 14 Life testing 14.1 Introduction 14.2 Hazard rate functions Remark on terminology 14.3 The exponential distribution in life testing 14.3.1 Simultaneous testing - stopping at the rth failure Remark 14.3.2 Sequential testing 14.3.3 Simultaneous testing - stopping by a fixed time Remark 14.3.4 The Bayesian approach Remark 14.4 A two-sample problem 14.5 The Weibull distribution in life testing 14.5.1 Parameter estimation by least squares Remarks Problems 15 Simulation, bootstrap statistical methods, and permutation tests 15.1 Introduction 15.2 Random numbers 15.2.1 The Monte Carlo simulation approach 15.3 The bootstrap method 15.4 Permutation tests 15.4.1 Normal approximations in permutation tests 15.4.2 Two-sample permutation tests 15.5 Generating discrete random variables 15.6 Generating continuous random variables 15.6.1 Generating a normal random variable 15.7 Determining the number of simulation runs in a Monte Carlo study Problems 16 Machine learning and big data 16.1 Introduction 16.2 Late flight probabilities 16.3 The naive Bayes approach 16.3.1 A variation of naive Bayes approach 16.4 Distance-based estimators. The k-nearest neighbors rule 16.4.1 A distance-weighted method 16.4.2 Component-weighted distances 16.5 Assessing the approaches 16.6 When characterizing vectors are quantitative 16.6.1 Nearest neighbor rules 16.6.2 Logistics regression 16.7 Choosing the best probability: a bandit problem Remarks Problems Appendix of Tables Index Back Cover
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.