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

Book Description

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.

Table of Contents

  1. Cover
  2. Title page
  3. Copyright page
  4. Preface
  5. Chapter 1 Variation
    1. 1.1 VARIATION
    2. 1.2 COLLECTING DATA
    3. 1.3 SUMMARIZING YOUR DATA
    4. 1.4 REPORTING YOUR RESULTS
    5. 1.5 TYPES OF DATA
    6. 1.6 DISPLAYING MULTIPLE VARIABLES
    7. 1.7 MEASURES OF LOCATION
    8. 1.8 SAMPLES AND POPULATIONS
    9. 1.9 SUMMARY AND REVIEW
  6. Chapter 2 Probability
    1. 2.1 PROBABILITY
    2. 2.2 BINOMIAL TRIALS
    3. *2.3 CONDITIONAL PROBABILITY
    4. 2.4 INDEPENDENCE
    5. 2.5 APPLICATIONS TO GENETICS
    6. 2.6 SUMMARY AND REVIEW
  7. Chapter 3 Two Naturally Occurring Probability Distributions
    1. 3.1 DISTRIBUTION OF VALUES
    2. 3.2 DISCRETE DISTRIBUTIONS
    3. 3.3 THE BINOMIAL DISTRIBUTION
    4. 3.4 MEASURING POPULATION DISPERSION AND SAMPLE PRECISION
    5. 3.5 POISSON: EVENTS RARE IN TIME AND SPACE
    6. 3.6 CONTINUOUS DISTRIBUTIONS
    7. 3.7 SUMMARY AND REVIEW
  8. Chapter 4 Estimation and the Normal Distribution
    1. 4.1 POINT ESTIMATES
    2. 4.2 PROPERTIES OF THE NORMAL DISTRIBUTION
    3. 4.3 USING CONFIDENCE INTERVALS TO TEST HYPOTHESES
    4. 4.4 PROPERTIES OF INDEPENDENT OBSERVATIONS
    5. 4.5 SUMMARY AND REVIEW
  9. Chapter 5 Testing Hypotheses
    1. 5.1 TESTING A HYPOTHESIS
    2. 5.2 ESTIMATING EFFECT SIZE
    3. 5.3 APPLYING THE T-TEST TO MEASUREMENTS
    4. 5.4 COMPARING TWO SAMPLES
    5. 5.5 WHICH TEST SHOULD WE USE?
    6. 5.6 SUMMARY AND REVIEW
  10. Chapter 6 Designing an Experiment or Survey
    1. 6.1 THE HAWTHORNE EFFECT
    2. 6.2 DESIGNING AN EXPERIMENT OR SURVEY
    3. 6.3 HOW LARGE A SAMPLE?
    4. 6.4 META-ANALYSIS
    5. 6.5 SUMMARY AND REVIEW
  11. Chapter 7 Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R
    1. 7.1 CREATING AND EDITING A DATA FILE
    2. 7.2 STORING AND RETRIEVING FILES FROM WITHIN R
    3. 7.3 RETRIEVING DATA CREATED BY OTHER PROGRAMS
    4. 7.4 USING R TO DRAW A RANDOM SAMPLE
  12. Chapter 8 Analyzing Complex Experiments
    1. 8.1 CHANGES MEASURED IN PERCENTAGES
    2. 8.2 COMPARING MORE THAN TWO SAMPLES
    3. 8.3 EQUALIZING VARIABILITY
    4. 8.4 CATEGORICAL DATA
    5. 8.5 MULTIVARIATE ANALYSIS
    6. 8.6 R PROGRAMMING GUIDELINES
    7. 8.7 SUMMARY AND REVIEW
  13. Chapter 9 Developing Models
    1. 9.1 MODELS
    2. 9.2 CLASSIFICATION AND REGRESSION TREES
    3. 9.3 REGRESSION
    4. 9.4 FITTING A REGRESSION EQUATION
    5. 9.5 PROBLEMS WITH REGRESSION
    6. 9.6 QUANTILE REGRESSION
    7. 9.7 VALIDATION
    8. 9.8 SUMMARY AND REVIEW
  14. Chapter 10 Reporting Your Findings
    1. 10.1 WHAT TO REPORT
    2. 10.2 TEXT, TABLE, OR GRAPH?
    3. 10.3 SUMMARIZING YOUR RESULTS
    4. 10.4 REPORTING ANALYSIS RESULTS
    5. 10.5 EXCEPTIONS ARE THE REAL STORY
    6. 10.6 SUMMARY AND REVIEW
  15. Chapter 11 Problem Solving
    1. 11.1 THE PROBLEMS
    2. 11.2 SOLVING PRACTICAL PROBLEMS
  16. Answers to Selected Exercises
  17. Index