This book offers an introduction to applied statistics through data analysis, integrating statistical computing methods. It covers robust and non-robust descriptive statistics used in each of four bivariate statistical models that are commonly used in research: ANOVA, proportions, regression, and logistic. The text teaches statistical inference principles using resampling methods (such as randomization and bootstrapping), covering methods for hypothesis testing and parameter estimation. These methods are applied to each statistical model introduced in preceding chapters.
Data analytic examples are used to teach statistical concepts throughout, and students are introduced to the R packages and functions required for basic data analysis in each of the four models. The text also includes introductory guidance to the fundamentals of data wrangling, as well as examples of write-ups so that students can learn how to communicate findings. Each chapter includes problems forpractice or assessment. Supplemental instructional videos are also available as an additional aid to instructors, or as a general resource to students.
This book is intended for an introductory or basic statistics course with an applied focus, or an introductory analytics course, at the undergraduate level in a two-year or four-year institution. This can be used for students with a variety of disciplinary backgrounds, from business, to the social sciences, to medicine. No sophisticated mathematical background is required.
Inhaltsverzeichnis
1. Foundations I: Introductory Data Analysis with R. - 2. Data Analysis in Bivariate Data: Foundations. - 3. Statistics and Data Analysis in an ANOVA Model. - 4. Statistics and Data Analysis in a Proportions Model. - 5. Statistics and Data Analysis in a Regression Model. - 6. Statistics and Data Analysis in a Logistic Model. - 7. Statistical Inference I: Randomization Methods for Hypothesis Testing. - 8. Statistical Inference II: Bootstrapping Methods for Parameter Estimation. - 9. Using Resampling Methods for Statistical Inference: Four Examples. - 10. Statistics and Data Analysis in a Pre-Post Design.
