Normality Testing in R for Data Analysis

Accurate statistical analysis hinges on understanding your data's distribution. This course provides the essential tools to evaluate if your data meets the critical assumption of normality. By the end of this course, you will be proficient in performing and interpreting normality tests using R, empowering you to make valid statistical inferences and build robust analytical models. What you'll learn: Understand the fundamental concepts of data distribution, statistical assumptions, and the importance of normality. Apply programmatic approaches to conduct common statistical normality tests, including Shapiro-Wilk and Kolmogorov-Smirnov, using R. Interpret graphical diagnostics such as Q-Q plots, histograms, and density plots to visually assess data distribution. Utilize modern R packages like dplyr for efficient data preparation and ggplot2 for creating insightful visualizations. Evaluate measures of skewness and kurtosis to comprehensively describe data shape. Develop a structured workflow for assessing normality and documenting your analytical decisions. The course starts with core statistical definitions and progresses through hands-on application of various normality tests and visualization techniques in R, emphasizing practical interpretation and decision-making. This course is ideal for beginners in statistics and R programming who want to build a solid foundation in data analysis without any prior experience required. Begin mastering data normality testing in R to enhance your analytical skills.