Introduction to Statistics Through Resampling Methods and R.
Title:
Introduction to Statistics Through Resampling Methods and R.
Author:
Good, Phillip I.
ISBN:
9781118497579
Personal Author:
Edition:
2nd ed.
Physical Description:
1 online resource (209 pages)
Contents:
Cover -- Title page -- Copyright page -- Contents -- Preface -- Chapter 1: Variation -- 1.1 Variation -- 1.2 Collecting Data -- 1.2.1 A Worked-Through Example -- 1.3 Summarizing Your Data -- 1.3.1 Learning to Use R -- 1.4 Reporting Your Results -- 1.4.1 Picturing Data -- 1.4.2 Better Graphics -- 1.5 Types of Data -- 1.5.1 Depicting Categorical Data -- 1.6 Displaying Multiple Variables -- 1.6.1 Entering Multiple Variables -- 1.6.2 From Observations to Questions -- 1.7 Measures of Location -- 1.7.1 Which Measure of Location? -- *1.7.2 The Geometric Mean -- 1.7.3 Estimating Precision -- 1.7.4 Estimating with the Bootstrap -- 1.8 Samples and Populations -- 1.8.1 Drawing a Random Sample -- *1.8.2 Using Data That Are Already in Spreadsheet Form -- 1.8.3 Ensuring the Sample Is Representative -- 1.9 Summary and Review -- Chapter 2: Probability -- 2.1 Probability -- 2.1.1 Events and Outcomes -- 2.1.2 Venn Diagrams -- 2.2 Binomial Trials -- 2.2.1 Permutations and Rearrangements -- *2.2.2 Programming Your Own Functions in R -- 2.2.3 Back to the Binomial -- 2.2.4 The Problem Jury -- *2.3 Conditional Probability -- 2.3.1 Market Basket Analysis -- 2.3.2 Negative Results -- 2.4 Independence -- 2.5 Applications to Genetics -- 2.6 Summary and Review -- Chapter 3: Two Naturally Occurring Probability Distributions -- 3.1 Distribution of Values -- 3.1.1 Cumulative Distribution Function -- 3.1.2 Empirical Distribution Function -- 3.2 Discrete Distributions -- 3.3 The Binomial Distribution -- *3.3.1 Expected Number of Successes in n Binomial Trials -- 3.3.2 Properties of the Binomial -- 3.4 Measuring Population Dispersion and Sample Precision -- 3.5 Poisson: Events Rare in Time and Space -- 3.5.1 Applying the Poisson -- 3.5.2 Comparing Empirical and Theoretical Poisson Distributions -- 3.5.3 Comparing Two Poisson Processes -- 3.6 Continuous Distributions.
3.6.1 The Exponential Distribution -- 3.7 Summary and Review -- Chapter 4: Estimation and the Normal Distribution -- 4.1 Point Estimates -- 4.2 Properties of the Normal Distribution -- 4.2.1 Student's t-Distribution -- 4.2.2 Mixtures of Normal Distributions -- 4.3 Using Confidence Intervals to Test Hypotheses -- 4.3.1 Should We Have Used the Bootstrap? -- 4.3.2 The Bias-Corrected and Accelerated Nonparametric Bootstrap -- 4.3.3 The Parametric Bootstrap -- 4.4 Properties of Independent Observations -- 4.5 Summary and Review -- Chapter 5: Testing Hypotheses -- 5.1 Testing a Hypothesis -- 5.1.1 Analyzing the Experiment -- 5.1.2 Two Types of Errors -- 5.2 Estimating Effect Size -- 5.2.1 Effect Size and Correlation -- 5.2.2 Using Confidence Intervals to Test Hypotheses -- 5.3 Applying the t-Test to Measurements -- 5.3.1 Two-Sample Comparison -- 5.3.2 Paired t-Test -- 5.4 Comparing Two Samples -- 5.4.1 What Should We Measure? -- 5.4.2 Permutation Monte Carlo -- 5.4.3 One- vs. Two-Sided Tests -- 5.4.4 Bias-Corrected Nonparametric Bootstrap -- 5.5 Which Test Should We Use? -- 5.5.1 p-Values and Significance Levels -- 5.5.2 Test Assumptions -- 5.5.3 Robustness -- 5.5.4 Power of a Test Procedure -- 5.6 Summary and Review -- Chapter 6: Designing an Experiment or Survey -- 6.1 The Hawthorne Effect -- 6.1.1 Crafting an Experiment -- 6.2 Designing an Experiment or Survey -- 6.2.1 Objectives -- 6.2.2 Sample from the Right Population -- 6.2.3 Coping with Variation -- 6.2.4 Matched Pairs -- 6.2.5 The Experimental Unit -- 6.2.6 Formulate Your Hypotheses -- 6.2.7 What Are You Going to Measure? -- 6.2.8 Random Representative Samples -- 6.2.9 Treatment Allocation -- 6.2.10 Choosing a Random Sample -- 6.2.11 Ensuring Your Observations Are Independent -- 6.3 How Large a Sample? -- 6.3.1 Samples of Fixed Size -- 6.3.2 Sequential Sampling -- 6.4 Meta-Analysis.
6.5 Summary and Review -- Chapter 7: Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R -- 7.1 Creating and Editing a Data File -- 7.2 Storing and Retrieving Files from within R -- 7.3 Retrieving Data Created by Other Programs -- 7.3.1 The Tabular Format -- 7.3.2 Comma-Separated Values -- 7.3.3 Data from Microsoft Excel -- 7.3.4 Data from Minitab, SAS, SPSS, or Stata Data Files -- 7.4 Using R to Draw a Random Sample -- Chapter 8: Analyzing Complex Experiments -- 8.1 Changes Measured in Percentages -- 8.2 Comparing More Than Two Samples -- 8.2.1 Programming the Multi-Sample Comparison in R -- *8.2.2 Reusing Your R Functions -- 8.2.3 What Is the Alternative? -- 8.2.4 Testing for a Dose Response or Other Ordered Alternative -- 8.3 Equalizing Variability -- 8.4 Categorical Data -- 8.4.1 Making Decisions with R -- 8.4.2 One-Sided Fisher's Exact Test -- 8.4.3 The Two-Sided Test -- 8.4.4 Testing for Goodness of Fit -- 8.4.5 Multinomial Tables -- 8.5 Multivariate Analysis -- 8.5.1 Manipulating Multivariate Data in R -- 8.5.2 Hotelling's T2 -- *8.5.3 Pesarin-Fisher Omnibus Statistic -- 8.6 R Programming Guidelines -- 8.7 Summary and Review -- Chapter 9: Developing Models -- 9.1 Models -- 9.1.1 Why Build Models? -- 9.1.2 Caveats -- 9.2 Classification and Regression Trees -- 9.2.1 Example: Consumer Survey -- 9.2.2 How Trees Are Grown -- 9.2.3 Incorporating Existing Knowledge -- 9.2.4 Prior Probabilities -- 9.2.5 Misclassification Costs -- 9.3 Regression -- 9.3.1 Linear Regression -- 9.4 Fitting a Regression Equation -- 9.4.1 Ordinary Least Squares -- 9.4.2 Types of Data -- 9.4.3 Least Absolute Deviation Regression -- 9.4.4 Errors-in-Variables Regression -- 9.4.5 Assumptions -- 9.5 Problems with Regression -- 9.5.1 Goodness of Fit versus Prediction -- 9.5.2 Which Model? -- 9.5.3 Measures of Predictive Success.
9.5.4 Multivariable Regression -- 9.6 Quantile Regression -- 9.7 Validation -- 9.7.1 Independent Verification -- 9.7.2 Splitting the Sample -- 9.7.3 Cross-Validation with the Bootstrap -- 9.8 Summary and Review -- Chapter 10: Reporting Your Findings -- 10.1 What to Report -- 10.1.1 Study Objectives -- 10.1.2 Hypotheses -- 10.1.3 Power and Sample Size Calculations -- 10.1.4 Data Collection Methods -- 10.1.5 Clusters -- 10.1.6 Validation Methods -- 10.2 Text, Table, or Graph? -- 10.3 Summarizing Your Results -- 10.3.1 Center of the Distribution -- 10.3.2 Dispersion -- 10.3.3 Categorical Data -- 10.4 Reporting Analysis Results -- 10.4.1 p-Values? Or Confidence Intervals? -- 10.5 Exceptions Are the Real Story -- 10.5.1 Nonresponders -- 10.5.2 The Missing Holes -- 10.5.3 Missing Data -- 10.5.4 Recognize and Report Biases -- 10.6 Summary and Review -- Chapter 11: Problem Solving -- 11.1 The Problems -- 11.2 Solving Practical Problems -- 11.2.1 Provenance of the Data -- 11.2.2 Inspect the Data -- 11.2.3 Validate the Data Collection Methods -- 11.2.4 Formulate Hypotheses -- 11.2.5 Choosing a Statistical Methodology -- 11.2.6 Be Aware of What You Don't Know -- 11.2.7 Qualify Your Conclusions -- Answers to Selected Exercises -- Index.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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Electronic Access:
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