Informationen zum Autor Michael J. Crawley, FRS, Department of Biological Sciences, Imperial College of Science, Technology and Medicine. Author of three bestselling Wiley statistics titles and five life science books. Klappentext "...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 RThis 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 likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.Includes numerous worked examples and exercises within each chapter. Zusammenfassung ". I know of no better book of its kind. Inhaltsverzeichnis Preface xiChapter 1 Fundamentals 1Everything Varies 2Significance 3Good and Bad Hypotheses 3Null Hypotheses 3p Values 3Interpretation 4Model Choice 4Statistical Modelling 5Maximum Likelihood 6Experimental Design 7The Principle of Parsimony (Occam's Razor) 8Observation, Theory and Experiment 8Controls 8Replication: It's the ns that Justify the Means 8How Many Replicates? 9Power 9Randomization 10Strong Inference 14Weak Inference 14How Long to Go On? 14Pseudoreplication 15Initial Conditions 16Orthogonal Designs and Non-Orthogonal Observational Data 16Aliasing 16Multiple Comparisons 17Summary of Statistical Models in R 18Organizing Your Work 19Housekeeping within R 20References 22Further Reading 22Chapter 2 Dataframes 23Selecting Parts of a Dataframe: Subscripts 26Sorting 27Summarizing the Content of Dataframes 29Summarizing by Explanatory Variables 30First Things First: Get to Know Your Data 31Relationships 34Looking for Interactions between Continuous Variables 36Graphics to Help with Multiple Regression 39Interactions Involving Categorical Variables 39Further Reading 41Chapter 3 Central Tendency 42Further Reading 49Chapter 4 Variance 50Degrees of Freedom 53Variance 53Variance: A Worked Example 55Variance and Sample Size 58Using Variance 59A Measure of Unreliability 60Confidence Intervals 61Bootstrap 62Non-constant Variance: Heteroscedasticity 65Further Reading 65Chapter 5 Single Samples 66Data Summary in the One-Sample Case 66The Normal Distribution 70Calculations Using z of the Normal Distribution 76Plots for Testing Normality of Single Samples 79Inference in the One-Sample Case 81Bootstrap in Hypothesis Testing with Single Samples 81Student's t Distribution 82Higher-Order Moments of a Distribution 83Skew 84Kurtosis 86Reference 87Further Reading 87Chapter 6 Two Samples 88Comparing Two Variances 88Comparing Two Means 90Student's t Test 91Wilcoxon Rank-Sum Test 95Tests on Paired Samples 97The Binomial Test 98Binomial Tests to Compare Two Proportions 100Chi-Squared Contingency Tables 100Fisher's Exact Test 105Correlation and Covariance 108Correlation and the Variance of Differences between Variables 110Scale-Dependent Correlations 112Reference 113Further Reading 113Chapter 7 Regression 114Linear Regression 116Linear Regression in R 117Calculations Involved in Linear Regression 122Partitioning Sums of Squares in Regression: SSY = SSR + SSE 125Measuring the Degree of Fit, r2 133Model Checking 134Transformation 135Polynomial Regression 140Non-Linear Regression 142Generalized Additive Models 146Influen...

Autorentext

Michael J. Crawley, FRS, Department of Biological Sciences, Imperial College of Science, Technology and Medicine. Author of three bestselling Wiley statistics titles and five life science books.

Klappentext

"...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 likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.

Inhalt
Preface xi Chapter 1 Fundamentals 1 Everything Varies 2 Significance 3 Good and Bad Hypotheses 3 Null Hypotheses 3 p Values 3 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 Pseudoreplicatio…