Statistical Testing with R
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| Title: | Statistical Testing with R |
|---|---|
| Description: | This book teaches statistics in a cheerful, straightforward manner, using the R open source programming language. Without mathematical formulae, the book assumes no knowledge of coding or statistics. Yet the lucid writing and clear structure takes the reader quickly and logically from beginner to graduate level. All tests come with worked examples. Tests include t tests, Mann-Whitney and Wilcoxon; one-way ANOVA, Kruskal-Wallis and Friedman; correlations and regression; categorical analyses including binomial, goodness of fit, chi square test of association, log-linear regression and McNemar; factorial ANOVA; MANOVA; principal components analysis and factor analysis; cluster analysis; logistic regression; the Kaplan-Meier curve and follow-up life tables; partial correlations, including non-parametric; an introduction to Bayesian statistics. The book takes a consistent approach to effect sizes, as well as covering data assumptions and confidence intervals. This should appeal to students, academics and professionals. |
| Authors: | Cole Davis |
| Resource Type: | eBook. |
| Subjects: | Social sciences--Statistical methods--Computer programs, Interactive computer systems, Statistics, R (Computer program language) |
| Categories: | LANGUAGE ARTS & DISCIPLINES / Library & Information Science / General |
| Database: | eBook Collection (EBSCOhost) |
| Abstract: | This book teaches statistics in a cheerful, straightforward manner, using the R open source programming language. Without mathematical formulae, the book assumes no knowledge of coding or statistics. Yet the lucid writing and clear structure takes the reader quickly and logically from beginner to graduate level. All tests come with worked examples. Tests include t tests, Mann-Whitney and Wilcoxon; one-way ANOVA, Kruskal-Wallis and Friedman; correlations and regression; categorical analyses including binomial, goodness of fit, chi square test of association, log-linear regression and McNemar; factorial ANOVA; MANOVA; principal components analysis and factor analysis; cluster analysis; logistic regression; the Kaplan-Meier curve and follow-up life tables; partial correlations, including non-parametric; an introduction to Bayesian statistics. The book takes a consistent approach to effect sizes, as well as covering data assumptions and confidence intervals. This should appeal to students, academics and professionals. |
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| ISBN: | 9781915500007 9781915500014 9781915500021 |
