Understanding and Applying Basic Statistical Methods Using R

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Detalles Bibliográficos
Autor principal: Wilcox, Rand R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2016.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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