Introduction to the Mann-Whitney U-test: An alternative to the independent T-test in R
In statistics, hypothesis tests are a crucial tool for determining whether differences between groups are significant. A widely used test for investigating differences between two independent groups is the independent T-test. However, this test assumes that the data is normally distributed and that the variances in the groups are equal. If these...Read More
Chi-square test: A comprehensive guide for use in statistics
The chi-square test (χ² test) is one of the basic statistical methods for analyzing whether there is a relationship between two categorical variables. This test is particularly useful when you are working with frequencies or contingency tables and want to check whether observed differences between groups are due to chance or indicate a real relationship. In...Read More
Introduction to Fixed Effects: A guide with R examples
Fixed effects play a central role in statistical analysis and in panel data analysis in particular. Fixed effects models are designed to control for unobserved heterogeneity by eliminating the effects of unobserved variables that remain constant over time. This makes them a powerful tool in the analysis of...Read More
Descriptive statistics in R with the table1 package: A guide
Descriptive statistics are an essential part of data analysis as they give you an initial overview of your data. They help to understand important features of the data, such as means, medians, standard deviations and distributions. There are many packages in R that can calculate these statistics, and one of the most user-friendly packages for this is the table1...Read More
Two-Way ANOVA: Understanding and interpreting the coefficients
One of the most powerful statistical methods for investigating the interaction effects between two factors on a dependent variable is the two-way ANOVA (two-factorial analysis of variance). This method is often used in experiments to test how different groups (e.g. treatment and control groups) perform under different conditions (e.g. different treatment and control...Read More
Linear Mixed Models vs. OLS Models: A Comparison and Selection Guide
In the world of statistics and data analysis, there are a variety of modeling approaches that researchers can use to analyze data and draw conclusions. Two commonly used methods are the Linear Mixed Model (LMM) and the Ordinary Least Squares (OLS) model. Although both models have their raison d'être in the...Read More
How do you check the condition of homogeneity of variance (homoscedasticity) for the linear regression model in R and SPSS?
Definition Multicollinearity is a condition where there is a strong correlation between the independent variables in a statistical model. This can occur in a linear regression model when there are multiple independent variables that correlate with each other, creating a type of redundancy in the data. The problem with multicollinearity is that it can affect the...Read More
How to perform a T-test in R: A comprehensive guide
Statistical tests are an indispensable tool in data analysis for testing hypotheses and drawing conclusions from data sets. One of the most commonly used tests is the T-test, which helps to assess whether the mean values of two groups differ statistically significantly from each other. R, a language and environment for statistical computing and graphics, provides...Read More
How to check the condition of normal distribution of residuals for the linear regression model in R and SPSS?
Definition In a linear regression, an attempt is made to model a linear dependence between a dependent variable y and one or more independent variables x. An important assumption here is that the residuals (the difference between the observed values of y and the predicted values of y) are normally distributed. A normal distribution of the...Read More