Author name: Erik Marsja

PhD in Psychology, Linköping University. Main interest is experimental and cognitive psychology. Enjoy programming in Python and R.

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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r standardization post

How to Standardize Data in R with scale() & dplyr

This tutorial covers the critical topic of standardizing data in R. Standardizing data involves transforming data such that the mean of the data becomes zero and the standard deviation becomes one. This technique is widely used in data analysis and modeling, including psychology, biology, and finance.
Through this post, you will learn about the circumstances when and why we may want or need to standardize data in R. We will also discuss what it means to standardize variables and which R functions are best suited for standardization. Additionally, the tutorial provides a step-by-step guide on how to standardize data in R. Starting with standardizing a vector, we move on to standardizing a matrix, standardizing data in a dataframe, and finally, standardizing only numeric columns in a dataframe.
Whether you are a seasoned data analyst, a research professional, or a student, this tutorial will equip you with essential skills to standardize data in R. These skills will enable you to understand better and analyze your data, paving the way for better insights and informed decision-making.

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