R

R statistical programming related stuff

Mastering SST & SSE in R: A Complete Guide for Analysts

Learn to calculate and interpret SSE/SSR and SST in R. Understand their significance, generate fake data, fit a linear model, and calculate SST and SSR using different methods, including ANOVA. Gain insights into evaluating model performance and enhance your statistical analysis skills. A comprehensive guide for data analysts and researchers.

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Report Correlation in APA Style using R: Text & Tables

In this post, you will learn how to report correlation according to APA. Adhering to APA (American Psychological Association) guidelines is crucial when reporting correlation analysis in academic research. Whether you are conducting research in psychology, cognitive hearing science, or cognitive science, APA style is often required by journals and conferences. This post will provide

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wide format to long format in r

Wide to Long in R using the pivot_longer & melt functions

In this post, we will learn how to transform data from wide to long in R. Wide-to-long format conversion is often an important data manipulation technique in data analysis. In R, we can use many packages and their functions to transform data from a wide to long format. These functions include the tidyr package’s pivot_longer()

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testing for normality in R

Test for Normality in R: Three Different Methods & Interpretation

In this blog post, you will learn how to test for the normality of residuals in R. Testing the normality of residuals is a step in data analysis. It helps determine if the residuals follow a normal distribution, which is important in many fields, including data science and psychology. Approximately normal residuals allow for powerful

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durbin watson in r

Durbin Watson Test in R: Step-by-Step incl. Interpretation

This blog will teach you how to carry out the Durbin-Watson Test in R. Have you ever run a linear regression model in R and wondered if the model’s assumptions hold? One common assumption of a linear regression model is the independence of observations, which means that the residuals (the differences between predicted and actual

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probit model in r

Probit Regression in R: Interpretation & Examples

This blog post will teach us how to use probit regression in R, a statistical modeling technique for analyzing binary response variables. Probit regression can be useful when the outcome variable is dichotomous. That is when the outcome variable takes only two possible values, such as “success” or “failure,” “yes” or “no,” or “tinnitus” or

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How to Create a Sankey Plot in R: 4 Methods

Sankey plots are an essential tool for data visualization in science and business. Whether you are exploring complex data flows, identifying patterns, or communicating insights, Sankey diagrams make it easy to visualize connections and gain meaningful insights. Discover four methods to create stunning Sankey plots in R and elevate your data analysis game!

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