In this post, we will explore various research questions that can be generated from a given dataset and determine the ideal chart types for visualizing the information.
In this post, we delve into the Student T-test. Learn to verify assumptions, use qqplots and the Shapiro-Wilk test for normality, the Levene test to assess variances, and conduct the T-test. Additionally, I will show you how to report the findings in academic articles.
This blogpost provides a tutorial on performing an ANOVA analysis using R for the Psych Rstats Club. ANOVA, an extension of the two-sample t-test, examines differences among means of multiple groups. Using the iris dataset, the tutorial covers assumptions, tests for normality and homogeneity of variances, ANOVA analysis with base R, post-hoc tests like Tukey\'s HSD, Bonferroni, and Holm corrections, and the rstatix package for simplified analysis.
This blog post focuses on data visualization in R for beginners. It demonstrates using base functions like plot() to create simple and effective visualizations. Topics covered include loading data, creating bar charts for categorical variables, scatterplots for numeric variables, and adding legends. The post emphasizes clear and practical visualizations rather than complex graphics.
This blog post focuses on creating functions to reduce repetitive code and improve efficiency. I demonstrate how to transform repeated code into a concise function using parameters and arguments, using specific date-based graphics creation as an example.
Learn how to work with quantitative data in this beginner\'s guide. Part 2 covers the essential measures of central tendency (mean, median, mode) using R. Discover how to calculate and interpret these statistics, handle missing values, and compare results by different groups. Improve your data analysis skills and stay tuned for the next post on basic data visualization.
New to working with quantitative data? Don\'t worry, we\'ve got you covered! In this blog post series, we provide a basic guide to help you navigate through the world of statistical analysis using R. Part 1 focuses on installing R and RStudio, importing data, and inspecting tables. Join us on this journey to enhance your statistical analysis skills!
Exploring Iterative Control Flow: Comparing R and Python's Loop Structures. Discover the syntax differences between these languages and delve into the power of for and while loops. From iterating through lists to handling conditions, learn how to optimize your code and enhance your programming skills.