Understanding and applying basic statistical methods using R
| Other Authors: | |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Hoboken, New Jersey :
Wiley
2017.
|
| Edition: | 1st ed |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009684435606719 |
Table of Contents:
- Cover
- Title Page
- Copyright
- Contents
- List of Symbols
- Preface
- About the Companion Website
- Chapter 1 Introduction
- 1.1 Samples Versus Populations
- 1.2 Comments on Software
- 1.3 R Basics
- 1.3.1 Entering Data
- 1.3.2 Arithmetic Operations
- 1.3.3 Storage Types and Modes
- 1.3.4 Identifying and Analyzing Special Cases
- 1.4 R Packages
- 1.5 Access to Data Used in this Book
- 1.6 Accessing More Detailed Answers to the Exercises
- 1.7 Exercises
- Chapter 2 Numerical Summaries of Data
- 2.1 Summation Notation
- 2.2 Measures of Location
- 2.2.1 The Sample Mean
- 2.2.2 The Median
- 2.2.3 Sample Mean versus Sample Median
- 2.2.4 Trimmed Mean
- 2.2.5 R function mean, tmean, and median
- 2.3 Quartiles
- 2.3.1 R function idealf and summary
- 2.4 Measures of Variation
- 2.4.1 The Range
- 2.4.2 R function Range
- 2.4.3 Deviation Scores, Variance, and Standard Deviation
- 2.4.4 R Functions var and sd
- 2.4.5 The Interquartile Range
- 2.4.6 MAD and the Winsorized Variance
- 2.4.7 R Functions winvar, winsd, idealfIQR, and mad
- 2.5 Detecting Outliers
- 2.5.1 A Classic Outlier Detection Method
- 2.5.2 The Boxplot Rule
- 2.5.3 The MAD-Median Rule
- 2.5.4 R Functions outms, outbox, and out
- 2.6 Skipped Measures of Location
- 2.6.1 R Function MOM
- 2.7 Summary
- 2.8 Exercises
- Chapter 3 Plots Plus More Basics on Summarizing Data
- 3.1 Plotting Relative Frequencies
- 3.1.1 R Functions table, plot, splot, barplot, and cumsum
- 3.1.2 Computing the Mean and Variance Based on the Relative Frequencies
- 3.1.3 Some Features of the Mean and Variance
- 3.2 Histograms and Kernel Density Estimators
- 3.2.1 R Function hist
- 3.2.2 What Do Histograms Tell Us?
- 3.2.3 Populations, Samples, and Potential Concerns about Histograms
- 3.2.4 Kernel Density Estimators
- 3.2.5 R Functions Density and Akerd.
- 3.3 Boxplots and Stem-and-Leaf Displays
- 3.3.1 R Function stem
- 3.3.2 Boxplot
- 3.3.3 R Function boxplot
- 3.4 Summary
- 3.5 Exercises
- Chapter 4 Probability and Related Concepts
- 4.1 The Meaning of Probability
- 4.2 Probability Functions
- 4.3 Expected Values, Population Mean and Variance
- 4.3.1 Population Variance
- 4.4 Conditional Probability and Independence
- 4.4.1 Independence and Dependence
- 4.5 The Binomial Probability Function
- 4.5.1 R Functions dbinom and pbinom
- 4.6 The Normal Distribution
- 4.6.1 Some Remarks about the Normal Distribution
- 4.6.2 The Standard Normal Distribution
- 4.6.3 Computing Probabilities for Any Normal Distribution
- 4.6.4 R Functions pnorm and qnorm
- 4.7 Nonnormality and The Population Variance
- 4.7.1 Skewed Distributions
- 4.7.2 Comments on Transforming Data
- 4.8 Summary
- 4.9 Exercises
- Chapter 5 Sampling Distributions
- 5.1 Sampling Distribution of P, the Proportion of Successes
- 5.2 Sampling Distribution of the Mean Under Normality
- 5.2.1 Determining Probabilities Associated with the Sample Mean
- 5.2.2 But Typically Is Not Known. Now What?
- 5.3 Nonnormality and the Sampling Distribution of the Sample Mean
- 5.3.1 Approximating the Binomial Distribution
- 5.3.2 Approximating the Sampling Distribution of the Sample Mean: The General Case
- 5.4 Sampling Distribution of the Median and 20% Trimmed Mean
- 5.4.1 Estimating the Standard Error of the Median
- 5.4.2 R Function msmedse
- 5.4.3 Approximating the Sampling Distribution of the Sample Median
- 5.4.4 Estimating the Standard Error of a Trimmed Mean
- 5.4.5 R Function trimse
- 5.4.6 Estimating the Standard Error When Outliers Are Discarded: A Technically Unsound Approach
- 5.5 The Mean Versus the Median and 20% Trimmed Mean
- 5.6 Summary
- 5.7 Exercises
- Chapter 6 Confidence Intervals.
- 6.1 Confidence Interval for the Mean
- 6.1.1 Computing a Confidence Interval Given 2
- 6.2 Confidence Intervals for the Mean Using s ( Not Known)
- 6.2.1 R Function t.test
- 6.3 A Confidence Interval for The Population Trimmed Mean
- 6.3.1 R Function trimci
- 6.4 Confidence Intervals for The Population Median
- 6.4.1 R Function msmedci
- 6.4.2 Underscoring a Basic Strategy
- 6.4.3 A Distribution-Free Confidence Interval for the Median Even When There Are Tied Values
- 6.4.4 R Function sint
- 6.5 The Impact of Nonnormality on Confidence Intervals
- 6.5.1 Student's T and Nonnormality
- 6.5.2 Nonnormality and the 20% Trimmed Mean
- 6.5.3 Nonnormality and the Median
- 6.6 Some Basic Bootstrap Methods
- 6.6.1 The Percentile Bootstrap Method
- 6.6.2 R Functions trimpb
- 6.6.3 Bootstrap-t
- 6.6.4 R Function trimcibt
- 6.7 Confidence Interval for The Probability of Success
- 6.7.1 Agresti-Coull Method
- 6.7.2 Blyth's Method
- 6.7.3 Schilling-Doi Method
- 6.7.4 R Functions acbinomci and binomLCO
- 6.8 Summary
- 6.9 Exercises
- Chapter 7 Hypothesis Testing
- 7.1 Testing Hypotheses about the Mean, Known
- 7.1.1 Details for Three Types of Hypotheses
- 7.1.2 Testing for Exact Equality and Tukey's Three-Decision Rule
- 7.1.3 p-Values
- 7.1.4 Interpreting p-Values
- 7.1.5 Confidence Intervals versus Hypothesis Testing
- 7.2 Power and Type II Errors
- 7.2.1 Power and p-Values
- 7.3 Testing Hypotheses about the mean, Not Known
- 7.3.1 R Function t.test
- 7.4 Student's T and Nonnormality
- 7.4.1 Bootstrap-t
- 7.4.2 Transforming Data
- 7.5 Testing Hypotheses about Medians
- 7.5.1 R Function msmedci and sintv2
- 7.6 Testing Hypotheses Based on a Trimmed Mean
- 7.6.1 R Functions trimci, trimcipb, and trimcibt
- 7.7 Skipped Estimators
- 7.7.1 R Function momci
- 7.8 Summary
- 7.9 Exercises.
- Chapter 8 Correlation and Regression
- 8.1 Regression Basics
- 8.1.1 Residuals and a Method for Estimating the Median of Y Given X
- 8.1.2 R function qreg and Qreg
- 8.2 Least Squares Regression
- 8.2.1 R Functions lsfit, lm, ols, plot, and abline
- 8.3 Dealing with Outliers
- 8.3.1 Outliers among the Independent Variable
- 8.3.2 Dealing with Outliers among the Dependent Variable
- 8.3.3 R Functions tsreg and tshdreg
- 8.3.4 Extrapolation Can Be Dangerous
- 8.4 Hypothesis Testing
- 8.4.1 Inferences about the Least Squares Slope and Intercept
- 8.4.2 R Functions lm, summary, and ols
- 8.4.3 Heteroscedcasticity: Some Practical Concerns and How to Address Them
- 8.4.4 R Function olshc4
- 8.4.5 Outliers among the Dependent Variable: A Cautionary Note
- 8.4.6 Inferences Based on the Theil-Sen Estimator
- 8.4.7 R Functions regci and regplot
- 8.5 Correlation
- 8.5.1 Pearson's Correlation
- 8.5.2 Inferences about the Population Correlation, p
- 8.5.3 R Functions pcor and pcorhc4
- 8.6 Detecting Outliers When Dealing with Two or More Variables
- 8.6.1 R Functions out and outpro
- 8.7 Measures of Association: Dealing with Outliers
- 8.7.1 Kendall's Tau
- 8.7.2 R Functions tau and tauci
- 8.7.3 Spearman's Rho
- 8.7.4 R Functions spear and spearci
- 8.7.5 Winsorized and Skipped Correlations
- 8.7.6 R Functions scor, scorci, scorciMC, wincor, and wincorci
- 8.8 Multiple Regression
- 8.8.1 Least Squares Regression
- 8.8.2 Hypothesis Testing
- 8.8.3 R Function olstest
- 8.8.4 Inferences Based on a Robust Estimator
- 8.8.5 R Function regtest
- 8.9 Dealing with Curvature
- 8.9.1 R Function lplot and rplot
- 8.10 Summary
- 8.11 Exercises
- Chapter 9 Comparing Two Independent Groups
- 9.1 Comparing Means
- 9.1.1 The Two-Sample Student's T Test
- 9.1.2 Violating Assumptions When Using Student's T.
- 9.1.3 Why Testing Assumptions Can Be Unsatisfactory
- 9.1.4 Interpreting Student's T When It Rejects
- 9.1.5 Dealing with Unequal Variances: Welch's Test
- 9.1.6 R Function t.test
- 9.1.7 Student's T versus Welch's Test
- 9.1.8 The Impact of Outliers When Comparing Means
- 9.2 Comparing Medians
- 9.2.1 A Method Based on the McKean-Schrader Estimator
- 9.2.2 A Percentile Bootstrap Method
- 9.2.3 R Functions msmed, medpb2, split, and fac2list
- 9.2.4 An Important Issue: The Choice of Method can Matter
- 9.3 Comparing Trimmed Means
- 9.3.1 R Functions yuen, yuenbt, and trimpb2
- 9.3.2 Skipped Measures of Location and Deleting Outliers
- 9.3.3 R Function pb2gen
- 9.4 Tukey's Three-Decision Rule
- 9.5 Comparing Variances
- 9.5.1 R Function comvar2
- 9.6 Rank-Based (Nonparametric) Methods
- 9.6.1 Wilcoxon-Mann-Whitney Test
- 9.6.2 R Function wmw
- 9.6.3 Handling Heteroscedasticity
- 9.6.4 R Functions cid and cidv2
- 9.7 Measuring Effect Size
- 9.7.1 Cohen's d
- 9.7.2 Concerns about Cohen's d and How They Might Be Addressed
- 9.7.3 R Functions akp.effect, yuenv2, and med.effect
- 9.8 Plotting Data
- 9.8.1 R Functions ebarplot, ebarplot.med, g2plot, and boxplot
- 9.9 Comparing Quantiles
- 9.9.1 R Function qcomhd
- 9.10 Comparing Two Binomial Distributions
- 9.10.1 Improved Methods
- 9.10.2 R Functions twobinom and twobicipv
- 9.11 A Method for Discrete or Categorical Data
- 9.11.1 R Functions disc2com, binband, and splotg2
- 9.12 Comparing Regression Lines
- 9.12.1 Classic ANCOVA
- 9.12.2 R Function CLASSanc
- 9.12.3 Heteroscedastic Methods for Comparing the Slopes and Intercepts
- 9.12.4 R Functions olsJ2 and ols2ci
- 9.12.5 Dealing with Outliers among the Dependent Variable
- 9.12.6 R Functions reg2ci, ancGpar, and reg2plot
- 9.12.7 A Closer Look at Comparing Nonparallel Regression Lines
- 9.12.8 R Function ancJN.
- 9.13 Summary.
