Advanced Statistics from an Elementary Point of View is a highly readable text that communicates the content of a course in mathematical statistics without imposing too much rigor. It clearly emphasizes the connection between statistics and probability, and helps students concentrate on statistical strategies without being overwhelmed by calculations. The book provides comprehensive coverage of descriptive statistics; detailed treatment of univariate and bivariate probability distributions; and thorough coverage of probability theory with numerous event classifications. This book is designed for statistics majors who are already familiar with introductory calculus and statistics, and can be used in either a one- or two-semester course. It can also serve as a statistics tutorial or review for working professionals. Students who use this book will be well on their way to thinking like a statistician in terms of problem solving and decision-making. Graduates who pursue careers in statistics will continue to find this book useful, due to numerous statistical test procedures (both parametric and non-parametric) and detailed examples.
Comprehensive coverage of descriptive statistics
More detailed treatment of univariate and bivariate probability distributions
Thorough coverage of probability theory with numerous event classifications
Professionals seeking exposure to data analysis. Also advanced undergraduates or beginning graduate students majoring in mathematics, statistics, engineering, actuarial science, economics/finance and life sciences
Table of Contents
1 Introduction 2 Elementary Descriptive Statistical Techniques 4 Random Variables and Probability Distributions 5 Bivariate Probability Distributions 6 Discrete Parametric Probability Distributions 7 Continuous Parametric Probability Distributions 8 Sampling and the Sampling Distribution of a Statistic 9 The Chi-Square, Student’s t, and Snedecor’s F Distributions 10 Point Estimation and Properties of Point Estimators 11 Interval Estimation and Confidence Interval Estimates 12 Tests of Parametric Statistical Hypotheses 13 Nonparametric Statistical Techniques 14 Testing Goodness of Fit 15 Testing Goodness of Fit: Contingency Tables 16 Bivariate Linear Regression and Correlation Appendix A Successive Difference to the Variance Solutions to Selected Exercises References and Suggested Reading Index