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The Resource Introduction to statistics through resampling methods and R, Phillip I. Good

Introduction to statistics through resampling methods and R, Phillip I. Good

Label
Introduction to statistics through resampling methods and R
Title
Introduction to statistics through resampling methods and R
Statement of responsibility
Phillip I. Good
Creator
Subject
Language
eng
Summary
"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"--
Member of
Has edition
Assigning source
Provided by publisher
Cataloging source
DLC
http://library.link/vocab/creatorName
Good, Phillip I
Dewey number
519.5/4
Index
index present
LC call number
QA278.8
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Resampling (Statistics)
  • Statistics as Topic
  • MATHEMATICS
  • Resampling (Statistics)
  • Statistik
  • Resampling
  • R
Label
Introduction to statistics through resampling methods and R, Phillip I. Good
Instantiates
Publication
Note
Includes indexes
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Control code
814389959
Edition
Second edition.
Extent
1 online resource
Form of item
online
Isbn
9781118497562
Lccn
2012043300
Media category
computer
Media MARC source
rdamedia
Media type code
c
http://library.link/vocab/ext/overdrive/overdriveId
  • 426251
  • 12df19b4-6468-46c5-ab77-d3b80b9bff13
Specific material designation
remote
System control number
(OCoLC)814389959
Label
Introduction to statistics through resampling methods and R, Phillip I. Good
Publication
Note
Includes indexes
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Control code
814389959
Edition
Second edition.
Extent
1 online resource
Form of item
online
Isbn
9781118497562
Lccn
2012043300
Media category
computer
Media MARC source
rdamedia
Media type code
c
http://library.link/vocab/ext/overdrive/overdriveId
  • 426251
  • 12df19b4-6468-46c5-ab77-d3b80b9bff13
Specific material designation
remote
System control number
(OCoLC)814389959

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