Essentials of Probability & Statistics for Engineers & Scientists (Pearson New International Edition)

Essentials of Probability & Statistics for Engineers & Scientists (Pearson New International Edition)

By: Sharon L. Myers (author), Raymond H. Myers (author), Ronald E. Walpole (author), Keying Ye (author)Paperback

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Description

For junior/senior undergraduates taking a one-semester probability and statistics course as applied to engineering, science, or computer science. This text covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. Students using this text should have the equivalent of the completion of one semester of differential and integral calculus.

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Contents

1. Introduction to Statistics and Probability 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 1.2 Sampling Procedures; Collection of Data 1.3 Discrete and Continuous Data. 1.4 Probability: Sample Space and Events Exercises 1.5 Counting Sample Points Exercises 1.6 Probability of an Event 1.7 Additive Rules Exercises 1.8 Conditional Probability, Independence, and the Product Rule Exercises 1.9 Bayes' Rule Exercises Review Exercises 2. Random Variables, Distributions, and Expectations 2.1 Concept of a Random Variable 2.2 Discrete Probability Distributions 2.3 Continuous Probability Distributions Exercises 2.4 Joint Probability Distributions Exercises 2.5 Mean of a Random Variable Exercises 2.6 Variance and Covariance of Random Variables. Exercises 2.7 Means and Variances of Linear Combinations of Random Variables Exercises Review Exercises 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 3. Some Probability Distributions 3.1 Introduction and Motivation 3.2 Binomial and Multinomial Distributions Exercises 3.3 Hypergeometric Distribution Exercises 3.4 Negative Binomial and Geometric Distributions 3.5 Poisson Distribution and the Poisson Process Exercises 3.6 Continuous Uniform Distribution 3.7 Normal Distribution 3.8 Areas under the Normal Curve 3.9 Applications of the Normal Distribution Exercises 3.10 Normal Approximation to the Binomial Exercises 3.11 Gamma and Exponential Distributions 3.12 Chi-Squared Distribution. Exercises Review Exercises 3.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 4. Sampling Distributions and Data Descriptions 4.1 Random Sampling 4.2 Some Important Statistics Exercises 4.3 Sampling Distributions 4.4 Sampling Distribution of Means and the Central Limit Theorem Exercises 4.5 Sampling Distribution of S2 4.6 t-Distribution 4.7 F-Distribution 4.8 Graphical Presentation Exercises Review Exercises 4.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 5. One- and Two-Sample Estimation Problems 5.1 Introduction 5.2 Statistical Inference 5.3 Classical Methods of Estimation. 5.4 Single Sample: Estimating the Mean 5.5 Standard Error of a Point Estimate 5.6 Prediction Intervals 5.7 Tolerance Limits Exercises 5.8 Two Samples: Estimating the Difference between Two Means 5.9 Paired Observations Exercises 5.10 Single Sample: Estimating a Proportion 5.11 Two Samples: Estimating the Difference between Two Proportions Exercises 5.12 Single Sample: Estimating the Variance Exercises Review Exercises 5.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 6. One- and Two-Sample Tests of Hypotheses. 6.1 Statistical Hypotheses: General Concepts 6.2 Testing a Statistical Hypothesis 6.3 The Use of P-Values for Decision Making in Testing Hypotheses Exercises 6.4 Single Sample: Tests Concerning a Single Mean 6.5 Two Samples: Tests on Two Means 6.6 Choice of Sample Size for Testing Means 6.7 Graphical Methods for Comparing Means Exercises 6.8 One Sample: Test on a Single Proportion. 6.9 Two Samples: Tests on Two Proportions Exercises 6.10 Goodness-of-Fit Test 6.11 Test for Independence (Categorical Data) 6.12 Test for Homogeneity 6.13 Two-Sample Case Study Exercises Review Exercises 6.14 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 7. One-Factor Experiments: General 7.1 Analysis-of-Variance Technique and the Strategy of Experimental Design 7.2 One-Way Analysis of Variance (One-Way ANOVA): Completely Randomized Design 7.3 Tests for the Equality of Several Variances Exercises 7.4 Multiple Comparisons Exercises 7.5 Concept of Blocks and the Randomized Complete Block Design Exercises 7.6 Random Effects Models 7.7 Case Study for One-Way Experiment Exercises Review Exercises 7.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 8. Linear Regression 8.1 Introduction to Linear Regression 8.2 The Simple Linear Regression (SLR) Model and the Least Squares Method. Exercises 8.3 Inferences Concerning the Regression Coefficients. 8.4 Prediction Exercises 8.5 Analysis-of-Variance Approach 8.6 Test for Linearity of Regression: Data with Repeated Observations Exercises 8.7 Diagnostic Plots of Residuals: Graphical Detection of Violation of Assumptions 8.8 Correlation 8.9 Simple Linear Regression Case Study. Exercises 8.10 Multiple Linear Regression and Estimation of the Coefficients Exercises 8.11 Inferences in Multiple Linear Regression Exercises Review Exercises 9. Factorial Experiments (Two or More Factors) 9.1 Introduction 9.2 Interaction in the Two-Factor Experiment 9.3 Two-Factor Analysis of Variance Exercises 9.4 Three-Factor Experiments. Exercises Review Exercises 9.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

Product Details

  • publication date: 16/08/2013
  • ISBN13: 9781292022536
  • Format: Paperback
  • Number Of Pages: 480
  • ID: 9781292022536
  • weight: 1038
  • ISBN10: 1292022531
  • edition: Pearson New International Edition

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  • Saver Delivery: Yes
  • 1st Class Delivery: Yes
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