Introduction to Statistics Through Resampling Methods and R, 2nd Edition

Wiley, 2013. – 216 p. – 2nd ed. – ISBN: 1118428218A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected "hints"—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods. Contents:Preface **Variation** Variation Collecting Data Summarizing Your Data Reporting Your Results Types of Data Displaying Multiple Variables Measures of Location Samples and Populations Summary and Review **Probability** Probability Binomial Trials Conditional Probability Independence Applications to Genetics Summary and Review **Two Naturally Occurring Probability Distributions** Distribution of Values Discrete Distributions The Binomial Distribution Measuring Population Dispersion and Sample Precision Poisson: Events Rare in Time and Space Continuous Distributions Summary and Review **Estimation and the Normal Distribution** Point Estimates Properties of the Normal Distribution Using Confidence Intervals to Test Hypotheses Properties of Independent Observations Summary and Review **Testing Hypotheses** Testing a Hypothesis Estimating Effect Size Applying the t-Test to Measurements Comparing Two Samples Which Test Should We Use? Summary and Review **Designing an Experiment or Survey** The Hawthorne Effect Designing an Experiment or Survey How Large a Sample? Meta-Analysis Summary and Review **Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R** Creating and Editing a Data File Storing and Retrieving Files from within R Retrieving Data Created by Other Programs Using R to Draw a Random Sample **Analyzing Complex Experiments** Changes Measured in Percentages Comparing More Than Two Samples Equalizing Variability Categorical Data Multivariate Analysis R Programming Guidelines Summary and Review **Developing Models** Models Classification and Regression Trees Regression Fitting a Regression Equation Problems with Regression Quantile Regression Validation Summary and Review **Reporting Your Findings** What to Report Text, Table, or Graph? Summarizing Your Results Reporting Analysis Results Exceptions Are the Real Story Summary and Review **Problem Solving**The Problems Solving Practical Problems Answers to Selected Exercises Index Wiley, 2013. – 216 p. – 2nd ed. – ISBN: 1118428218 A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected "hints"—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods. Contents: Preface Variation Variation Collecting Data Summarizing Your Data Reporting Your Results Types of Data Displaying Multiple Variables Measures of Location Samples and Populations Summary and Review Probability Probability Binomial Trials Conditional Probability Independence Applications to Genetics Summary and Review Two Naturally Occurring Probability Distributions Distribution of Values Discrete Distributions The Binomial Distribution Measuring Population Dispersion and Sample Precision Poisson: Events Rare in Time and Space Continuous Distributions Summary and Review Estimation and the Normal Distribution Point Estimates Properties of the Normal Distribution Using Confidence Intervals to Test Hypotheses Properties of Independent Observations Summary and Review Testing Hypotheses Testing a Hypothesis Estimating Effect Size Applying the t-Test to Measurements Comparing Two Samples Which Test Should We Use? Summary and Review Designing an Experiment or Survey The Hawthorne Effect Designing an Experiment or Survey How Large a Sample? Meta-Analysis Summary and Review Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R Creating and Editing a Data File Storing and Retrieving Files from within R Retrieving Data Created by Other Programs Using R to Draw a Random Sample Analyzing Complex Experiments Changes Measured in Percentages Comparing More Than Two Samples Equalizing Variability Categorical Data Multivariate Analysis R Programming Guidelines Summary and Review Developing Models Models Classification and Regression Trees Regression Fitting a Regression Equation Problems with Regression Quantile Regression Validation Summary and Review Reporting Your Findings What to Report Text, Table, or Graph? Summarizing Your Results Reporting Analysis Results Exceptions Are the Real Story Summary and Review Problem Solving The Problems Solving Practical Problems Answers to Selected Exercises Index Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. It includes all the tools needed to facilitate quick learning, including: more than 250 exercises with selected hints, multiple explanations of basic concepts, real-life applications in client-, and statistics-related disciplines, a companion FTP site with data sets and R programs, and more. "Intended for class use or self-study, the second addition of this text aspires like the first to introduce statistical methodology to a wide audience, simply and intuitively, through resampling from the data at hand. The methodology proceeds from chapter to chapter from the simple to the complex"-- Provided by publisher