Learning Statistics with R
Free

Learning Statistics with R

By Daniel Navarro
Free
Book Description
Table of Contents
  • Preface
  • I Background
    • Why do we learn statistics?
      • On the psychology of statistics
      • The cautionary tale of Simpson's paradox
      • Statistics in psychology
      • Statistics in everyday life
      • There's more to research methods than statistics
    • A brief introduction to research design
      • Introduction to psychological measurement
      • Scales of measurement
      • Assessing the reliability of a measurement
      • The ``role'' of variables: predictors and outcomes
      • Experimental and non-experimental research
      • Assessing the validity of a study
      • Confounds, artifacts and other threats to validity
      • Summary
  • II An introduction to R
    • Getting started with R
      • Installing R
      • Typing commands at the R console
      • Doing simple calculations with R
      • Storing a number as a variable
      • Using functions to do calculations
      • Letting Rstudio help you with your commands
      • Storing many numbers as a vector
      • Storing text data
      • Storing ``true or false'' data
      • Indexing vectors
      • Quitting R
      • Summary
    • Additional R concepts
      • Using comments
      • Installing and loading packages
      • Managing the workspace
      • Navigating the file system
      • Loading and saving data
      • Useful things to know about variables
      • Factors
      • Data frames
      • Lists
      • Formulas
      • Generic functions
      • Getting help
      • Summary
  • III Working with data
    • Descriptive statistics
      • Measures of central tendency
      • Measures of variability
      • Skew and kurtosis
      • Getting an overall summary of a variable
      • Descriptive statistics separately for each group
      • Standard scores
      • Correlations
      • Handling missing values
      • Summary
    • Drawing graphs
      • An overview of R graphics
      • An introduction to plotting
      • Histograms
      • Stem and leaf plots
      • Boxplots
      • Scatterplots
      • Bar graphs
      • Saving image files using R and Rstudio
      • Summary
    • Pragmatic matters
      • Tabulating and cross-tabulating data
      • Transforming and recoding a variable
      • A few more mathematical functions and operations
      • Extracting a subset of a vector
      • Extracting a subset of a data frame
      • Sorting, flipping and merging data
      • Reshaping a data frame
      • Working with text
      • Reading unusual data files
      • Coercing data from one class to another
      • Other useful data structures
      • Miscellaneous topics
      • Summary
    • Basic programming
      • Scripts
      • Loops
      • Conditional statements
      • Writing functions
      • Implicit loops
      • Summary
  • IV Statistical theory
    • Introduction to probability
      • How are probability and statistics different?
      • What does probability mean?
      • Basic probability theory
      • The binomial distribution
      • The normal distribution
      • Other useful distributions
      • Summary
    • Estimating unknown quantities from a sample
      • Samples, populations and sampling
      • The law of large numbers
      • Sampling distributions and the central limit theorem
      • Estimating population parameters
      • Estimating a confidence interval
      • Summary
    • Hypothesis testing
      • A menagerie of hypotheses
      • Two types of errors
      • Test statistics and sampling distributions
      • Making decisions
      • The p value of a test
      • Reporting the results of a hypothesis test
      • Running the hypothesis test in practice
      • Effect size, sample size and power
      • Some issues to consider
      • Summary
  • V Statistical tools
    • Categorical data analysis
      • The 2 goodness-of-fit test
      • The 2 test of independence (or association)
      • The continuity correction
      • Effect size
      • Assumptions of the test(s)
      • The most typical way to do chi-square tests in R
      • The Fisher exact test
      • The McNemar test
      • What's the difference between McNemar and independence?
      • Summary
    • Comparing two means
      • The one-sample z-test
      • The one-sample t-test
      • The independent samples t-test (Student test)
      • The independent samples t-test (Welch test)
      • The paired-samples t-test
      • One sided tests
      • Using the t.test() function
      • Effect size
      • Checking the normality of a sample
      • Testing non-normal data with Wilcoxon tests
      • Summary
    • Comparing several means (one-way ANOVA)
      • An illustrative data set
      • How ANOVA works
      • Running an ANOVA in R
      • Effect size
      • Multiple comparisons and post hoc tests
      • Assumptions of one-way ANOVA
      • Checking the homogeneity of variance assumption
      • Removing the homogeneity of variance assumption
      • Checking the normality assumption
      • Removing the normality assumption
      • On the relationship between ANOVA and the Student t test
      • Summary
    • Linear regression
      • What is a linear regression model?
      • Estimating a linear regression model
      • Multiple linear regression
      • Quantifying the fit of the regression model
      • Hypothesis tests for regression models
      • Testing the significance of a correlation
      • Regarding regression coefficients
      • Assumptions of regression
      • Model checking
      • Model selection
      • Summary
    • Factorial ANOVA
      • Factorial ANOVA 1: balanced designs, no interactions
      • Factorial ANOVA 2: balanced designs, interactions allowed
      • Effect size, estimated means, and confidence intervals
      • Assumption checking
      • The F test as a model comparison
      • ANOVA as a linear model
      • Different ways to specify contrasts
      • Post hoc tests
      • The method of planned comparisons
      • Factorial ANOVA 3: unbalanced designs
      • Summary
  • VI Endings, alternatives and prospects
    • Bayesian statistics
      • Probabilistic reasoning by rational agents
      • Bayesian hypothesis tests
      • Why be a Bayesian?
      • Bayesian analysis of contingency tables
      • Bayesian t-tests
      • Bayesian regression
      • Bayesian ANOVA
      • Summary
    • Epilogue
      • The undiscovered statistics
      • Learning the basics, and learning them in R
      • References
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