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."—TechnometricsWritten 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 applicationsIntroduction 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 ProblemsAnswers to Selected Exercises
Index
