Introduction to statistics through resampling methods and R /

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"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 compl...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Good, Phillip I.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc., [2013]
Edición:Second edition.
Temas:
Resampling (Statistics)
Statistics.
Statistics as Topic
Rééchantillonnage (Statistique)
Statistiques.
Statistique.
statistics.
MATHEMATICS > Probability & Statistics > General.
Statistics
Statistik
Resampling
R > Programm
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.

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