Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output.
The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as...
Author(s): Daniel J. Denis
Edition: 1
Publisher: Wiley
Year: 2020
Language: English
Commentary: Book stitched together from individual chapters. Bookmarks incomplete and page numbers not identical to print.
Pages: 384
1 Introduction to Applied Statistics
2 Introduction to R and Computational Statistics
3 Exploring Data with R: Essential Graphics and Visualization
4 Means, Correlations, Counts: Drawing Inferences Using Easy-to-Implement Statistical Tests
5 Power Analysis and Sample Size Estimation Using R
6 Analysis of Variance: Fixed Effects, Random Effects, Mixed Models, and Repeated Measures
7 Simple and Multiple Linear Regression
8 Logistic Regression and the Generalized Linear Model
9 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
10 Principal Component Analysis
11 Exploratory Factor Analysis
12 Cluster Analysis
13 Nonparametric Tests
