| Description |
1 online resource |
| Contents |
Chapter 1 Fundamentals 1 -- Everything Varies 2 -- Significance 3 -- Good and Bad Hypotheses 3 -- Null Hypotheses 3 -- p Values 4 -- Interpretation 4 -- Model Choice 4 -- Statistical Modelling 5 -- Maximum Likelihood 6 -- Experimental Design 7 -- The Principle of Parsimony (Occam's Razor) 8 -- Observation, Theory and Experiment 8 -- Controls 8 -- Replication: It's the ns that Justify the Means 8 -- How Many Replicates? 9 -- Power 9 -- Randomization 10 -- Strong Inference 14 -- Weak Inference 14 -- How Long to Go On? 14 -- Pseudoreplication 15 -- Initial Conditions 16 -- Orthogonal Designs and Non-Orthogonal Observational Data 16 -- Aliasing 16 -- Multiple Comparisons 17 -- Summary of Statistical Models in R 18 -- Organizing Your Work 19 -- Housekeeping within R 20 -- References 22 -- Further Reading 22 -- Chapter 2 Dataframes 23 -- Selecting Parts of a Dataframe: Subscripts 26 -- Sorting 27 -- Summarizing the Content of Dataframes 29 -- Summarizing by Explanatory Variables 30 -- First Things First: Get to Know Your Data 31 -- Relationships 34 -- Looking for Interactions between Continuous Variables 36 -- Graphics to Help with Multiple Regression 39 -- Interactions Involving Categorical Variables 39 -- Further Reading 41 -- Chapter 3 Central Tendency 42 -- Further Reading 49 -- Chapter 4 Variance 50 -- Degrees of Freedom 53 -- Variance 53 -- Variance: A Worked Example 55 -- Variance and Sample Size 58 -- Using Variance 59 -- A Measure of Unreliability 60 -- Confidence Intervals 61 -- Bootstrap 62 -- Non-constant Variance: Heteroscedasticity 65 -- Further Reading 65 -- Chapter 5 Single Samples 66 -- Data Summary in the One-Sample Case 66 -- The Normal Distribution 70 -- Calculations Using z of the Normal Distribution 76 -- Plots for Testing Normality of Single Samples 79 -- inference in the One-Sample Case 81 -- Bootstrap in Hypothesis Testing with Single Samples 81 -- Student's t Distribution 82 -- Higher-Order Moments of a Distribution 83 -- Skew 84 -- Kurtosis 86 -- Reference 87 -- Further Reading 87 -- Chapter 6 Two Samples 88 -- Comparing Two Variances 88 -- Comparing Two Means 90 -- Student's t Test 91 -- Wilcoxon Rank-Sum Test 95 -- Tests on Paired Samples 97 -- The Binomial Test 98 -- Binomial Tests to Compare Two Proportions 100 -- Chi-Squared Contingency Tables 100 -- Fisher's Exact Test 105 -- Correlation and Covariance 108 -- Correlation and the Variance of Differences between Variables 110 -- Scale-Dependent Correlations 112 -- Reference 113 -- Further Reading 113 -- Chapter 7 Regression 114 -- Linear Regression 116 -- Linear Regression in R 117 -- Calculations Involved in Linear Regression 122 -- Partitioning Sums of Squares in Regression: SSY = SSR + SSE 125 -- Measuring the Degree of Fit, r² 133 -- Model Checking 134 -- Transformation 135 -- Polynomial Regression 140 -- Non-Linear Regression 142 -- Generalized Additive Models 146 -- Influence 148 -- Further Reading 149 -- Chapter 8 Analysis of Variance 150 -- One-Way ANOVA 150 -- Shortcut Formulas 157 -- Effect Sizes 159 -- Plots for Interpreting One-Way ANOVA 162 -- Factorial Experiments 168 -- Pseudoreplication: Nested Designs and Split Plots 173 -- Split-Plot Experiments 174 -- Random Effects and Nested Designs 176 -- Fixed or Random Effects? 177 -- Removing the Pseudoreplication 178 -- Analysis of Longitudinal Data 178 -- Derived Variable Analysis 179 -- Dealing with Pseudoreplication 179 -- Variance Components Analysis (VCA) 183 -- References 184 -- Further Reading 184 -- Further Reading -- Chapter 9 Analysis of Covariance 185 -- Further Reading 192 -- Chapter 10 Multiple Regression 193 -- The Steps Involved in Model Simplification 195 -- Caveats 196 -- Order of Deletion 196 -- Carrying Out a Multiple Regression 197 -- A Trickier Example 203 -- Further Reading 211 -- Chapter 11 Contrasts 212 -- Contrast Coefficients 213 -- An Example of Contrasts in R 214 -- A Priori Contrasts 215 -- Treatment Contrasts 216 -- Model Simplification by Stepwise Deletion 218 -- Contrast Sums of Squares by Hand 222 -- The Three Kinds of Contrasts Compared 224 -- Reference 225 -- Further Reading 225 -- Chapter 12 Other Response Variables 226 -- Introduction to Generalized Linear Models 228 -- The Error Structure 229 -- The Linear Predictor 229 -- Fitted Values 230 -- A General Measure of Variability 230 -- The Link Function 231 -- Canonical Link Functions 232 -- Akaike's Information Criterion (AIC) as a Measure of the Fit of a Model 233 -- Further Reading 233 -- Chapter 13 Count Data 234 -- A Regression with Poisson Errors 234 -- Analysis of Deviance with Count Data 237 -- The Danger of Contingency Tables 244 -- Analysis of Covariance with Count Data 247 -- Frequency Distributions 250 -- Further Reading 255 -- Chapter 14 Proportion Data 256 -- Analyses of Data on One and Two Proportions 257 -- Averages of Proportions 257 -- Count Data on Proportions 257 -- Odds 259 -- Overdispersion and Hypothesis Testing 260 -- Applications 261 -- Logistic Regression with Binomial Errors 261 -- Proportion Data with Categorical Explanatory Variables 264 -- Analysis of Covariance with Binomial Data 269 -- Further Reading 272 -- Chapter 15 Binary Response Variable 273 -- Incidence Functions 275 -- ANCOVA with a Binary Response Variable 279 -- Further Reading 284 -- Chapter 16 Death and Failure Data 285 -- Survival Analysis with Censoring 287 -- Further Reading 290 |
| Summary |
"" ... I know of no better book of its kind ... "" (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likel |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on November 26, 2014) |
| Subject |
Mathematical statistics -- Textbooks
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R (Computer program language)
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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Mathematical statistics
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R (Computer program language)
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| Genre/Form |
Textbooks
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Textbooks.
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| Form |
Electronic book
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| ISBN |
9781118941119 |
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111894111X |
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9781118941102 |
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1118941101 |
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1118941098 |
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9781118941096 |
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