Introductory Statistics
- 5th Edition - September 1, 2026
- Latest edition
- Author: Sheldon M. Ross
- Language: English
Introductory Statistics, Fifth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures… Read more
Purchase options
Introductory Statistics, Fifth 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 they are 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, vaccines and other health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others.
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 they are 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, vaccines and other health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others.
- Uses the free statistical package R throughout. Not only is R used to do standard statistical analysis but students are also taught to write their own R programs when needed
- 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 independent learning using real issues and real data, e.g. stock price models, health issues, gender issues, sports, and scientific fraud
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-long course in Probability and Statistics
1. Introduction to Statistics
2. Describing Data Sets
3. Using Statistics to Summarize Data
4. Probability
5. Discrete Random Variables
6. Normal Random Variables
7. Distribution of sampling Statistics
8. Statistical Estimation
9. Testing Statistical Hypotheses
10. Hypothesis Testing concerning two populations
11. Analysis of Variance
12. Linear Regression
13. Chi-squared goodness of fit tests
14. Nonparametric Hypothesis Tests
15. Quality Control
16. Machine Learning and Big Data
APPENDIX A
APPENDIX B
ANSWERS TO ODD-NUMBERED PROBLEMS
Index
2. Describing Data Sets
3. Using Statistics to Summarize Data
4. Probability
5. Discrete Random Variables
6. Normal Random Variables
7. Distribution of sampling Statistics
8. Statistical Estimation
9. Testing Statistical Hypotheses
10. Hypothesis Testing concerning two populations
11. Analysis of Variance
12. Linear Regression
13. Chi-squared goodness of fit tests
14. Nonparametric Hypothesis Tests
15. Quality Control
16. Machine Learning and Big Data
APPENDIX A
APPENDIX B
ANSWERS TO ODD-NUMBERED PROBLEMS
Index
Review of the previous edition:
"The coverage is careful and slow, with many worked examples and plenty of problems, half of which have answers. ...Illuminating examples abound. Those who are less than wholly confident about any of the material will find it a rich and unthreatening resource of information and also of questions (even if they are almost all derived from a US context). I have been looking for some time for a properly academic superior to M.J. Moroney’s invaluable if outdated Facts from figures which I have used for forty years, and this would seem to fill the bill."—The Mathematical Gazette
"There are some interesting topics included that are not in most introductory stats texts, such as the Gini index, bandit problems, and quality control."—MAA Reviews
"The coverage is careful and slow, with many worked examples and plenty of problems, half of which have answers. ...Illuminating examples abound. Those who are less than wholly confident about any of the material will find it a rich and unthreatening resource of information and also of questions (even if they are almost all derived from a US context). I have been looking for some time for a properly academic superior to M.J. Moroney’s invaluable if outdated Facts from figures which I have used for forty years, and this would seem to fill the bill."—The Mathematical Gazette
"There are some interesting topics included that are not in most introductory stats texts, such as the Gini index, bandit problems, and quality control."—MAA Reviews
- Edition: 5
- Latest edition
- Published: September 1, 2026
- Language: English
SR
Sheldon M. Ross
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
Affiliations and expertise
Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA