Description
Book SynopsisIntroduction to R programming and RStudio Integrated Development Environment (IDE).- Working with Data in R: Objects, Vectors, Factors, Packages and Libraries, and Data Visualization.- Test of Normality and Reliability of Data in R.- Choosing between Parametric and Non-Parametric Tests in Statistical Data Analysis.- Understanding Dependent and Independent Variables in Research Experiments and Hypothesis Testing.- Understanding the Different Types of Statistical Data Analysis and Methods.- Regression Analysis in R: Linear and Logistic Regression.- T-test Statistics in R: Independent samples, Paired sample, and One sample ttests.- Analysis of Variance (ANOVA) in R: One-way and Two-way ANOVA.- Chi-squared (X2) Statistical Test in R.- Mann Whitney U test and Kruskal Wallis H test Statistics in R.- Correlation Tests in R: Pearson cor, Kendall's tau, and Spearman's rho.- Wilcoxon Statistics in R: Signed-Rank test and Rank-Sum test.
