Introductory Statistics - Edition 4 - By Sheldon M. Ross Elsevier Inspection Copies

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Introductory Statistics,

Edition 4

By Sheldon M. Ross

Publication Date: 08 Feb 2017

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Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods.

Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data. Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions, and examples.

Applications and examples refer to real-world issues, such as gun control, stock price models, health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others. Examples relating to data mining techniques using the number of Google queries or Twitter tweets are also considered.

For this fourth edition, new topical coverage includes sections on Pareto distribution and the 80-20 rule, Benford's law, added material on odds and joint distributions and correlation, logistic regression, A-B testing, and more modern (big data) examples and exercises.

Key Features

Includes new section on Pareto distribution and the 80-20 rule, Benford’s law, odds, joint distribution and correlation, logistic regression, A-B testing, and examples from the world of analytics and big data
Comprehensive edition that includes the most commonly used statistical software packages (SAS, SPSS, Minitab)
Presents a unique, historical perspective, profiling prominent statisticians and historical events to motivate learning by including interest and context
Provides exercises and examples that help guide the student towards indpendent learning using real issues and real data, e.g. stock price models, health issues, gender issues, sports, and scientific fraud

About the author

By Sheldon M. Ross, Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA

Chapter 1: Introduction to Statistics

Abstract
1.1. Introduction
1.2. The Nature of Statistics
1.3. Populations and Samples
1.4. A Brief History of Statistics
Key Terms
The Changing Definition of Statistics
Review Problems

Chapter 2: Describing Data Sets

Abstract
2.1. Introduction
2.2. Frequency Tables and Graphs
Problems
2.3. Grouped Data and Histograms
Problems
2.4. Stem-and-Leaf Plots
Problems
2.5. Sets of Paired Data
Problems
2.6. Some Historical Comments
Key Terms
Summary
Review Problems

Chapter 3: Using Statistics to Summarize Data Sets

Abstract
3.1. Introduction
3.2. Sample Mean
Problems
3.3. Sample Median
Problems
Problems
3.4. Sample Mode
Problems
3.5. Sample Variance and Sample Standard Deviation
Problems
3.6. Normal Data Sets and the Empirical Rule
Problems
3.7. Sample Correlation Coefficient
Problems
3.8. The Lorenz Curve and Gini Index
Problems
3.9. Using R
Key Terms
Summary
Review Problems

Chapter 4: Probability

Abstract
4.1. Introduction
4.2. Sample Space and Events of an Experiment
Problems
4.3. Properties of Probability
Problems
4.4. Experiments Having Equally Likely Outcomes
Problems
4.5. Conditional Probability and Independence
Problems
*4.6. Bayes' Theorem
Problems
*4.7. Counting Principles
Problems
Key Terms
Summary
Review Problems

Chapter 5: Discrete Random Variables

Abstract
5.1. Introduction
5.2. Random Variables
Problems
5.3. Expected Value
Problems
5.4. Variance of Random Variables
Problems
5.5. Jointly Distributed Random Variables
Problems
5.6. Binomial Random Variables
Problems
*5.7. Hypergeometric Random Variables
Problems
*5.8. Poisson Random Variables
Problems
5.9. Using R to calculate Binomial and Poisson Probabilities
Key Terms
Summary
Review Problems

Chapter 6: Normal Random Variables

Abstract
6.1. Introduction
6.2. Continuous Random Variables
Problems
6.3. Normal Random Variables
Problems
6.4. Probabilities Associated with a Standard Normal Random Variable
Problems
6.5. Finding Normal Probabilities: Conversion to the Standard Normal
6.6. Additive Property of Normal Random Variables
Problems
6.7. Percentiles of Normal Random Variables
Problems
6.8. Calculating Normal Probabilities with R
Key Terms
Summary
Review Problems

Chapter 7: Distributions of Sampling Statistics

Abstract
7.1. A Preview
7.2. Introduction
7.3. Sample Mean
Problems
7.4. Central Limit Theorem
Problems
7.5. Sampling Proportions from a Finite Population
Problems
7.6. Distribution of the Sample Variance of a Normal Population
Problems
Key Terms
Summary
Review Problems

Chapter 8: Estimation

Abstract
8.1. Introduction
8.2. Point Estimator of a Population Mean
Problems
8.3. Point Estimator of a Population Proportion
Problems
Problems
8.4. Estimating a Population Variance
Problems
8.5. Interval Estimators of the Mean ofa Normal Population with Known Population Variance
Problems
8.6. Interval Estimators of the Mean ofa Normal Population with Unknown Population Variance
Problems
8.7. Interval Estimators of a Population Proportion
Problems
8.8. Use of R
Key Terms
Summary
Review Problems

Chapter 9: Testing Statistical Hypotheses

Abstract
9.1. Introduction
9.2. Hypothesis Tests and Significance Levels
Problems
9.3. Tests Concerning the Mean of a Normal Population: Case of Known Variance
Problems
Problems
9.4. The t Test for the Mean of a Normal Population: Case of Unknown Variance
Problems
9.5. Hypothesis Tests Concerning Population Proportions
Problems
9.6. Use of R in Running a One Sample t-test
Key Terms
Summary
Review Problems and Proposed Case Studies

Chapter 10: Hypothesis Tests Concerning Two Populations

Abstract
10.1. Introduction
10.2. Testing Equality of Means of Two Normal Populations: Case of Known Variances
Problems
10.3. Testing Equality of Means: Unknown Variances and Large Sample Sizes
Problems
10.4. Testing Equality of Means: Small-Sample Tests when the Unknown Population Variances Are Equal
Problems
10.5. Paired-Sample t Test
Problems
10.6. Testing Equality of Population Proportions
Problems
10.7. Use of R in Running a Two Sample t-Test
Key Terms
Summary
Review Problems

Chapter 11: Analysis of Variance

Abstract
11.1. Introduction
11.2. One-Factor Analysis of Variance
Problems
11.3. Two-Factor Analysis of Variance: Introduction and Parameter Estimation
Problems
11.4. Two-Factor Analysis of Variance: Testing Hypotheses
Problems
11.5. Final Comments
Key Terms
Summary
Review Problems

Chapter 12: Linear Regression

Abstract
12.1. Introduction
12.2. Simple Linear Regression Model
Problems
12.3. Estimating the Regression Parameters
Problems
12.4. Error Random Variable
Problems
12.5. Testing the Hypothesis that ß=0
Problems
12.6. Regression to the Mean
Problems
12.7. Prediction Intervals for Future Responses
Problems
12.8. Coefficient of Determination
Problems
12.9. Sample Correlation Coefficient
Problems
12.10. Analysis of Residuals: Assessingthe Model
Problems
12.11. Multiple Linear Regression Model
Problems
12.12. Logistic Regression
12.13. Use of R in Regression
Key Terms
Summary
Review Problems

Chapter 13: Chi-Squared Goodness-of-Fit Tests

Abstract
13.1. Introduction
13.2. Chi-Squared Goodness-of-Fit Tests
Problems
13.3. Testing for Independence in Populations Classified According to Two Characteristics
Problems
13.4. Testing for Independence in Contingency Tables with Fixed Marginal Totals
Problems
13.5. Use of R
Key Terms
Summary
Review Problems

Chapter 14: Nonparametric Hypotheses Tests

Abstract
14.1. Introduction
14.2. Sign Test
Problems
14.3. Signed-Rank Test
Problems
14.4. Rank-Sum Test for Comparing Two Populations
Problems
14.5. Runs Test for Randomness
Problems
14.6. Testing the Equality of Multiple Probability Distributions
Problems
14.7. Permutation Tests
Problems
Key Terms
Summary
Review Problems

Chapter 15: Quality Control

Abstract
15.1. Introduction
15.2. The X? Control Chart for Detecting a Shift in the Mean
Problems
Problems
15.3. Control Charts for Fraction Defective
Problems
15.4. Exponentially Weighted Moving-Average Control Charts
Problems
15.5. Cumulative-Sum Control Charts
Problems
Key Terms
Summary
Review Problems

Chapter 16: Machine Learning and Big Data

Abstract
16.1. Introduction
16.2. Late Flight Probabilities
16.3. The Naive Bayes Approach
Problems
16.4. Distance Based Estimators the k-Nearest Neighbors Rule
Problems
16.5. Assessing the Approaches
Problems
16.6. Choosing the Best Probability: A Bandit Problem
Problems

Appendix A: A Data Set

Appendix B: Mathematical Preliminaries

B.1. Summation
B.2. Absolute Value
B.3. Set Notation

Appendix C: How to Choose a Random Sample

Appendix D: Tables

Appendix E: Programs

Answers to Odd-Numbered Problems

Chapter 1 Problems
Chapter 2 Review
Chapter 3 Review
Chapter 4 Review
Chapter 5 Review
Chapter 6 Review
Chapter 7 Review
Chapter 8 Review
Chapter 9 Review
Chapter 10 Review
Chapter 11 Review
Chapter 12 Review
Chapter 13 Review
Chapter 14 Review
Chapter 15 Review

Index

Introductory Statistics

1. Introduction to Statistics
2. Describing Data Sets
3. Using Statistics to Summarize Data Sets
4. Probability
5. Discrete Random Variables
6. Normal Random Variables
7. Distributions of Sampling Statistics
8. Estimation
9. Testing Statistical Hypotheses
10. Hypotheses Tests Concerning Two Populations
11. Analysis of Variance
12. Linear Regression
13. Chi-squared Goodness of Fit Tests
14. Nonparametric Hypotheses
15. Quality Control

ISBN: 9780128043172

Page Count: 828

Retail Price : £97.99

Ross, Introduction to Probability and Statistics for Engineers and Scientists, 2009, 9780123704832, $105.00
Forthofer, Biostatistics, 2^nd edition, 2007, 9780123694928, $101.00
Rao, Handbook of Statistics v. 26, 2006, 9780444521033, $350.00

Access to teacher/student resources is available to registered users with approved inspection copies or confirmed adoptions. To review this material, please request an inspection copy.

This text is written for the introductory non-calculus based statistics course offered in mathematics and/or statistics departments for undergraduate students of any major who take a semester course in basic Statistics or a year course in Probability and Statistics

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