Statistical vocabulary -- Reasoning with probability -- Probabilities in the long run -- Introducing the logic of inference using confidence intervals -- Bayesian and traditional hypothesis testing -- Comparing groups and analyzing experiments -- Associations between variables -- Linear multiple regression -- Interactions in ANOVA and regression -- Logistic regression -- Analyzing change over time -- Dealing with too many variables -- All together now.
Summary:
Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources.-- Provided by Publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.