This brief course in statistical inference was extensively class tested by the author at the University of Illinois, and it requires only a basic familiarity with probability and matrix and linear algebra. Ninety problems with solutions make it an ideal choice for self-study as well as a helpful review of a wide range of statistical formulas with applications in business, government, public administration, and other fields.Â
The first eight chapters review results from basic probability that are important to statistics, including transformation of random variables, Jacobians, moment-generating functions, sampling from a normal population, order statistics, and the central limit theorem. Additional subjects include estimation, confidence intervals, hypothesis testing, correlation, nonparametric statistics, and many other topics.
The first eight chapters review results from basic probability that are important to statistics, including transformation of random variables, Jacobians, moment-generating functions, sampling from a normal population, order statistics, and the central limit theorem. Additional subjects include estimation, confidence intervals, hypothesis testing, correlation, nonparametric statistics, and many other topics.
