Introductory Statistics (4th Ed.)
Author: Ross Sheldon M.Language: Anglais
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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.
- 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), ISM, SSM, and an online graphing calculator manual
- 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
1. Preface 2. Introduction to Statistics 3. Describing Data Sets Using Statistics to Summarize 4. Data Sets Probability 5. Discrete Random Variables 6. Normal Random Variables 7. Distributions of Sampling 8. Statistics Estimation Testing 9. Statistical Hypotheses 10. Hypothesis Tests Concerning Two Populations 11. Analysis of Variance Linear Regression 12. Chi-Squared Goodness of Fit Tests 13. Nonparametric Hypotheses 14. Tests 15. Quality Control 16. Appendices
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.