Book description
Introducing the tools of statistics and probability from the ground up
An understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining stepbystep how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences.
Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and realworld data sets, the book incorporates clear instructions on how to use the statistical packages Minitab and Microsoft Office Excel to analyze various data sets. The book also features:
Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices
A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method
Comprehensive guidance on the design of experiments, including randomized block designs, one and twoway layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology
A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP routines and results
Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet triedandtrue, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate realworld data in engineering and the natural sciences.
Table of contents
 Cover
 Title Page
 Copyright
 Dedication
 Preface
 Chapter 1: Introduction

Part I

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®
 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

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 and Poisson Distribution by the Normal Distribution
 5.8 A Test of Normality
 5.9 Probability Models Commonly Used in Reliability Theory
 5.10 A Case Study
 5.11 Using JMP
 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
 8.10 A Case Study
 8.11 Using JMP

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 Population Having Known Variance
 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 Normal Distributions with Unknown Variances
 9.8 Testing Population Proportions
 9.9 Tests Concerning the Variance of a Normal Population
 9.10 Tests Concerning the Ratio of Variances of Two Normal Populations
 9.11 Testing of Statistical Hypotheses Using Confidence Intervals
 9.12 Sequential Tests of Hypotheses
 9.13 Case Studies
 9.14 Using JMP

Chapter 2: Describing Data Graphically and Numerically

Part II
 Chapter 10: Elements of Reliability Theory
 Chapter 11: Statistical Quality Control–Phase I Control Charts
 Chapter 12: Statistical Quality Control—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 σ2
 15.4 Further Inferences Concerning Regression Coefficients (β0, β1), E(Y), and Y
 15.5 Tests of Hypotheses for β0 and β1
 15.6 Analysis of Variance Approach to Simple Linear Regression Analysis
 15.7 Residual Analysis
 15.8 Transformations
 15.9 Inference About ρ
 15.10 A Case Study (Load Cell Calibration)
 15.11 Using JMP

Chapter 16: Multiple Linear Regression Analysis
 16.1 Introduction
 16.2 Multiple Linear Regression Models
 16.3 Estimation of Regression Coefficients
 16.4 Multiple Linear Regression Model Using Quantitative and Qualitative Predictor Variables
 16.5 Standardized Regression Coefficients
 16.6 Building Regression Type Prediction Models
 16.7 Residual Analysis and certain criteria for model selection
 16.8 Logistic Regression
 16.9 Case Studies
 16.10 Using JMP
 Chapter 17: Analysis of Variance
 Chapter 18: The 2k Factorial Designs
 Appendices
 Index
Product information
 Title: Statistics and Probability with Applications for Engineers and Scientists
 Author(s):
 Release date: May 2013
 Publisher(s): Wiley
 ISBN: 9781118464045
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