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Index
Cover Title Page Copyright 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
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