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Statistics : an introduction using R.

Author: Michael J Crawley
Publisher: Hoboken : John Wiley & Sons, 2011.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling.
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Genre/Form: Electronic books
Textbooks
Additional Physical Format: Print version:
Crawley, Michael J.
Statistics : An Introduction using R.
Hoboken : John Wiley & Sons, ©2011
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Michael J Crawley
ISBN: 9780470022993 047002299X 1283397323 9781283397322
OCLC Number: 769343037
Description: 1 online resource (342 pages)
Contents: Statistics: An Introduction using R; Contents; Preface; Chapter 1 Fundamentals; Everything Varies; Significance; Good and Bad Hypotheses; Null Hypotheses; p Values; Interpretation; Statistical Modelling; Maximum Likelihood; Experimental Design; The Principle of Parsimony (Occam's Razor); Observation, Theory and Experiment; Controls; Replication: It's the n's 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. Chapter 2 Dataframes: Selecting Parts of a Dataframe: Subscripts; Sorting; Saving Your Work; Tidying Up; Chapter 3 Central Tendency; Getting Help in R; Chapter 4 Variance; Degrees of Freedom; Variance; A Worked Example; Variance and Sample Size; Using Variance; A Measure of Unreliability; Confidence Intervals; Bootstrap; 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; Chapter 6 Two Samples; Comparing Two Variances; Comparing Two Means; Student's t-test; Wilcox on Rank Sum Test; Tests on Paired Samples; The Sign Test; Binomial Tests to Compare Two Proportions; Chi-square Contingency Tables; Fisher's Exact Test; Correlation and Covariance; Data Dredging; Partial Correlation; Correlation and the Variance of Differences Between Variables; Scale-dependent Correlations; Kolmogorov-Smirnov Test; Chapter 7 Statistical Modelling; The Steps Involved in Model Simplification; Caveats. Order of Deletion model Formulae in R; Interactions Between Explanatory Variables; Multiple Error Terms; The Intercept as Parameter 1; Update in Model Simplification; Examples of R Model Formulae; Model Formulae for Regression; GLMs: Generalized Linear Models; The Error Structure; The Linear Predictor; Fitted Values; The Link Function; Canonical Link Functions; Proportion Data and Binomial Errors; Count Data and Poisson Errors; GAMs: Generalized Additive Models; Model Criticism; Summary of Statistical Models in R; Model Checking; Non-constant Variance: Heteroscedasticity. Non-Normality of Errors Influence; Leverage; Mis-specified Model; Chapter 8 Regression; Linear Regression; Linear Regression in R; Error Variance in Regression: SSY = SSR + SSE; Measuring the Degree of Fit, r2; Model Checking; Polynomial Regression; Non-linear Regression; Testing for Humped Relationships; Generalized Additive Models (gams); Chapter 9 Analysis of Variance; One-way Anova; Shortcut Formula; Effect Sizes; Plots for Interpreting One-way Anova; Factorial Experiments; Pseudoreplication: Nested Designs and Split Plots; Split-plot Experiments; Random Effects and Nested Designs.

Abstract:

Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling.

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