Understanding Statistical Concepts Using S-plus

Understanding Statistical Concepts Using S-plus

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About the Book

Written as a supplemental text for an introductory or intermediate statistics course, this book is organized along the lines of many popular statistics texts. The chapters provide a good conceptual understanding of basic statistics and include exercises that use S-PLUS simulation programs. Each chapter lists a set of objectives and a summary.

The book offers a rich insight into how probability has shaped statistical procedures in the behavioral sciences, as well as a brief history behind the creation of various statistics. Computational skills are kept to a minimum by including S-PLUS programs that run the exercises in the chapters. Students are not required to master the writing of S-PLUS programs, but explanations of how the programs work and program output are included in each chapter. S-PLUS is an advanced statistical package that has an extensive library of functions, which offer flexibility in writing customized routines.

The S-PLUS functions provide the capability of programming object and dialog windows, which are commonly used in Windows software applications. The S-PLUS program also contains pull-down menus for the statistical analysis of data.

A ZIP file containing programs that work in S-PLUS 6.2 for use with this book is available for download from http://www.psypress.com/resources/9780805836233.zip - please note that these scripts will only run in S-PLUS 6.2 and not later versions due to changes in the programming language syntax.

Table of Contents

Contents: Preface. Part I: Introduction and Statistical Theory. Statistical Theory. Generating Random Numbers. Frequency Distributions. Stem and Leaf Plots. Population Distributions. Measures of Central Tendency. Measures of Dispersion. Sample Size Effects. Tchebysheff Inequality Theorem. Normal Bell-Shaped Curve. Part II: Probability and Probability Distributions. Probability. Joint Probability. Addition Law of Probability. Multiplication Law of Probability. Conditional Probability. Combinations and Permutations. Part III: Monte Carlo and Statistical Distributions. Binomial Distribution. Monte Carlo Simulation. Normal Distribution. t Distribution. Chi-square Distribution. F Distribution. Part IV: Sampling and Inference. Sampling Distributions. Central Limit Theorem. Confidence Intervals. Hypothesis Testing. Type I Error. Type II Error. Part V: Hypothesis Testing in Research. z Test Statistic for Proportions. Chi-square Test Statistic. t Test for Mean Differences. Analysis of Variance. Correlation. Linear Regression. Part VI: Replicability of Findings. Cross Validation. Jackknife. Bootstrap. Meta-Analysis. Significance Testing vs. Practical Importance. Appendix.

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