Book description
All statistical concepts are supported by a large number of examples using data encountered in real life situations; and the text illustrates how the statistical packages MINITAB®, Microsoft Excel ®, and JMP® may be used to aid in the analysis of various data sets. The text also covers an appropriate and understandable level of the design of experiments. This includes randomized block designs, one and twoway designs, Latin square designs, factorial designs, response surface designs, and others. This text is suitable for a one or twosemester calculusbased undergraduate statistics course for engineers and scientists, and the presentation of material gives instructors flexibility to pick and choose topics for their particular courses.
Table of contents
 Cover Page
 Title Page
 Copyright
 About the Preliminary Edition
 2: Describing Data Graphically and Numerically
 Contents
 PREFACE
 Chapter 1: Introduction

Chapter 2: Describing Data Graphically and Numerically
 2.1 Getting Started With Statistics
 2.2 Classification of Various Types of Data
 2.3 Frequency Distribution Tables for Qualitative and Quantitative Data
 2.4 Graphical Description of Qualitative and Quantitative Data
 2.5 Numerical Measures of Quantitative Data
 2.6 Numerical Measures of Grouped Data
 2.7 Measures of Relative Position
 2.8 BoxWhisker Plot
 2.9 Measures of Association
 2.10 Case Studies
 2.11 Using JMP
 Review Practice Problems
 Chapter 3: Elements of Probability

Chapter 4: Discrete Random Variables and Some Important Discrete Probability Distributions
 4.1 Graphical Descriptions of Discrete Distributions
 4.2 Mean and Variance of a Discrete Random Variable
 4.3 The Discrete Uniform Distribution
 4.4 The Hypergeometric Distribution
 4.5 The Bernoulli Distribution
 4.6 The Binomial Distribution
 4.7 The Multinomial Distribution
 4.8 The Poisson Distribution
 4.9 The Negative Binomial Distribution
 4.10 Some Derivations and Proofs (Optional)
 4.11 A Case Study
 4.12 Using JMP
 Review Practice Problems

Chapter 5: Continuous Random Variables and Some Important Continuous Probability Distributions
 5.1 Continuous Random Variables
 5.2 Mean and Variance of Continuous Random Variables
 5.3 Chebychev's Inequality
 5.4 The Uniform Distribution
 5.5 The Normal Distribution
 5.6 Distribution of Linear Combination of Independent Normal Variables
 5.7 Approximation of the Binomial Distribution by the Normal Distribution
 5.8 A Test of Normality
 5.9 The Lognormal Distribution
 5.10 The Exponential Distribution
 5.11 The Gamma Distribution
 5.12 The Weibull Distribution
 5.13 A Case Study
 5.14 Using JMP
 Review Practice Problems
 Chapter 6: Distribution of Functions of Random Variables
 Chapter 7: Sampling Distributions

Chapter 8: Estimation of Population Parameters
 8.1 Introduction
 8.2 Point Estimators for the Population Mean and Variance
 8.3 Interval Estimators for the Mean μ of a Normal Population
 8.4 Interval Estimators for the Difference of Means of Two Normal Populations
 8.5 Interval Estimators for the Variance of a Normal Population
 8.6 Interval Estimator for the Ratio of Variances of Two Normal Populations
 8.7 Point and Interval Estimators for the Parameters of Binomial Populations
 8.8 Determination of Sample Size
 8.9 Some Supplemental Information (Optional)
 8.10 A Case Study
 8.11 Using JMP
 Review Practice Problems

Chapter 9: Hypothesis Testing
 9.1 Introduction
 9.2 Basic Concepts of Testing a Statistical Hypothesis
 9.3 Tests Concerning the Mean of a Normal Distribution Having Known Variance Case of a LeftSided (OneTail) Test
 9.4 Tests Concerning the Mean of a Normal Population Having Unknown Variance
 9.5 Large Sample Theory
 9.6 Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances
 9.7 Tests Concerning the Difference of Means of Two Populations Having Distributions with Unknown Variances
 9.8 Testing Population Proportions
 9.9 Tests Concerning the Variance of a Normal Distribution
 9.10 Tests Concerning the Ratio of Variances of Two Normal Populations
 9.11 An Alternative Technique for Testing of Statistical Hypotheses: Using Confidence Intervals
 9.12 Sequential Tests of Hypotheses (Optional)
 9.13 Case Studies
 9.14 Using JMP
 Review Practice Problems
 Chapter 10: Elements of Reliability Theory
 Chapter 11: Statistical Quality Control and Phase I Control Charts
 Chapter 12: Statistical Quality Control and Phase II Control Charts
 Chapter 13: Analysis of Categorical Data
 Chapter 14: Nonparametric Tests

Chapter 15: Simple Linear Regression Analysis
 15.1 Introduction
 15.2 Fitting the Simple Linear Regression Model
 15.3 Unbiased Estimator of σ 2
 15.4 Further Inferences Concerning Regression Coefficients ( β 0 , β 1 ), E ( Y ), and Y
 15.5 Tests of Hypotheses for β 0 and β 1
 15.6 Analysis of Variance Approach to Simple Regression Analysis
 15.7 Residual Analysis
 15.8 Transformations
 15.9 Inference About ρ
 15.10 A Case Study
 15.11 Using JMP
 Review Practice Problems

Chapter 16: Multiple Linear Regression Analysis
 16.1 Introduction
 16.2 The Multiple Linear Regression Model
 16.3 Estimation of Regression Coefficients
 16.4 The Multiple Linear Regression Model Using Qualitative or Categorical Predictor Variables
 16.5 Standardized Regression Coefficients
 16.6 Building RegressionType Prediction Models
 16.7 Residual Analysis
 16.8 Logistic Regression
 16.9 Case Studies
 16.10 Using JMP
 Review Practice Problems
 Chapter 17: Analysis of Variance
 Chapter 18: The 2 k Factorial Designs
 Chapter 19: Response Surfaces
 Appendices
 Appendix B: Answers to Selected Problems
 Appendix C: Bibliography
 Index
Product information
 Title: Statistics and Probability with Applications for Engineers and Scientists, Preliminary Edition
 Author(s):
 Release date: September 2011
 Publisher(s): Wiley
 ISBN: 9781118098721
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