Statistics: An Introduction Using R -- Contents -- Preface -- Chapter 1: Fundamentals -- Everything Varies -- Significance -- Good and Bad Hypotheses -- Null Hypotheses -- p Values -- Interpretation -- Model Choice -- Statistical Modelling -- Maximum Likelihood -- Experimental Design -- The Principle of Parsimony (Occam's Razor) -- Observation, Theory and Experiment -- Controls -- Replication: It's the ns that Justify the Means -- How Many Replicates? -- Power -- Randomization -- Strong Inference -- Weak Inference -- How Long to Go On? -- Pseudoreplication -- Initial Conditions -- Orthogonal Designs and Non-Orthogonal Observational Data -- Aliasing -- Multiple Comparisons -- Summary of Statistical Models in R -- Organizing Your Work -- Housekeeping within R -- References -- Further Reading -- Chapter 2: Dataframes -- Selecting Parts of a Dataframe: Subscripts -- Sorting -- Summarizing the Content of Dataframes -- Summarizing by Explanatory Variables -- First Things First: Get to Know Your Data -- Relationships -- Looking for Interactions between Continuous Variables -- Graphics to Help with Multiple Regression -- Interactions Involving Categorical Variables -- Further Reading -- Chapter 3: Central Tendency -- Further Reading -- Chapter 4: Variance -- Degrees of Freedom -- Variance -- Variance: A Worked Example -- Variance and Sample Size -- Using Variance -- A Measure of Unreliability -- Confidence Intervals -- Bootstrap -- Non-constant Variance: Heteroscedasticity -- Further Reading -- Chapter 5: Single Samples -- Data Summary in the One-Sample Case -- The Normal Distribution -- Calculations Using z of the Normal Distribution -- Plots for Testing Normality of Single Samples -- Inference in the One-Sample Case -- Bootstrap in Hypothesis Testing with Single Samples -- Student's t Distribution -- Higher-Order Moments of a Distribution -- Skew.

Kurtosis -- Reference -- Further Reading -- Chapter 6: Two Samples -- Comparing Two Variances -- Comparing Two Means -- Student's t Test -- Wilcoxon Rank-Sum Test -- Tests on Paired Samples -- The Binomial Test -- Binomial Tests to Compare Two Proportions -- Chi-Squared Contingency Tables -- Fisher's Exact Test -- Correlation and Covariance -- Correlation and the Variance of Differences between Variables -- Scale-Dependent Correlations -- Reference -- Further Reading -- Chapter 7: Regression -- Linear Regression -- Linear Regression in R -- Calculations Involved in Linear Regression -- Partitioning Sums of Squares in Regression: SSY = SSR + SSE -- Measuring the Degree of Fit, r2 -- Model Checking -- Transformation -- Polynomial Regression -- Non-Linear Regression -- Generalized Additive Models -- Influence -- Further Reading -- Chapter 8: Analysis of Variance -- One-Way ANOVA -- Shortcut Formulas -- Effect Sizes -- Plots for Interpreting One-Way ANOVA -- Factorial Experiments -- Pseudoreplication: Nested Designs and Split Plots -- Split-Plot Experiments -- Random Effects and Nested Designs -- Fixed or Random Effects? -- Removing the Pseudoreplication -- Analysis of Longitudinal Data -- Derived Variable Analysis -- Dealing with Pseudoreplication -- Variance Components Analysis (VCA) -- References -- Further Reading -- Chapter 9: Analysis of Covariance -- Further Reading -- Chapter 10: Multiple Regression -- The Steps Involved in Model Simplification -- Caveats -- Order of Deletion -- Carrying Out a Multiple Regression -- A Trickier Example -- Further Reading -- Chapter 11: Contrasts -- Contrast Coefficients -- An Example of Contrasts in R -- A Priori Contrasts -- Treatment Contrasts -- Model Simplification by Stepwise Deletion -- Contrast Sums of Squares by Hand -- The Three Kinds of Contrasts Compared -- Reference -- Further Reading.

Chapter 12: Other Response Variables -- Introduction to Generalized Linear Models -- The Error Structure -- The Linear Predictor -- Fitted Values -- A General Measure of Variability -- The Link Function -- Canonical Link Functions -- Akaike's Information Criterion (AIC) as a Measure of the Fit of a Model -- Further Reading -- Chapter 13: Count Data -- A Regression with Poisson Errors -- Analysis of Deviance with Count Data -- The Danger of Contingency Tables -- Analysis of Covariance with Count Data -- Frequency Distributions -- Further Reading -- Chapter 14: Proportion Data -- Analyses of Data on One and Two Proportions -- Averages of Proportions -- Count Data on Proportions -- Odds -- Overdispersion and Hypothesis Testing -- Applications -- Logistic Regression with Binomial Errors -- Proportion Data with Categorical Explanatory Variables -- Analysis of Covariance with Binomial Data -- Further Reading -- Chapter 15: Binary Response Variable -- Incidence Functions -- ANCOVA with a Binary Response Variable -- Further Reading -- Chapter 16: Death and Failure Data -- Survival Analysis with Censoring -- Further Reading -- Appendix: Essentials of the R Language -- R as a Calculator -- Built-in Functions -- Numbers with Exponents -- Modulo and Integer Quotients -- Assignment -- Rounding -- Infinity and Things that Are Not a Number (NaN) -- Missing Values (NA) -- Operators -- Creating a Vector -- Named Elements within Vectors -- Vector Functions -- Summary Information from Vectors by Groups -- Subscripts and Indices -- Working with Vectors and Logical Subscripts -- Addresses within Vectors -- Trimming Vectors Using Negative Subscripts -- Logical Arithmetic -- Repeats -- Generate Factor Levels -- Generating Regular Sequences of Numbers -- Matrices -- Character Strings -- Writing Functions in R -- Arithmetic Mean of a Single Sample.

Median of a Single Sample -- Loops and Repeats -- The ifelse Function -- Evaluating Functions with apply -- Testing for Equality -- Testing and Coercing in R -- Dates and Times in R -- Calculations with Dates and Times -- Understanding the Structure of an R Object Using str -- Reference -- Further Reading -- Index -- End User License Agreement.