UID:
almafu_9959328927002883
Format:
1 online resource
Edition:
Second edition.
ISBN:
9781118497579
,
1118497570
,
9781118497562
,
1118497562
,
9781118497678
,
1118497678
,
9781118497593
,
1118497597
,
1118428218
,
9781118428214
,
9781283950015
,
1283950014
Uniform Title:
Introduction to statistics through resampling methods and R/S-PLUS
Content:
"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"--
Note:
Includes indexes.
,
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.
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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.
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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.
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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.
Additional Edition:
Revion of: Good, Phillip I. Introduction to statistics through resampling methods and R/S-PLUS.
Additional Edition:
Print version: Good, Phillip I. Introduction to statistics through resampling methods and R. Hoboken, New Jersey : John Wiley & Sons, Inc., [2013] ISBN 9781118428214
Language:
English
Keywords:
Electronic books.
;
Electronic books.
;
Electronic books.
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118497593
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118497593
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118497593
