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

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

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

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

If you carry out a linear regression, you should also check whether the requirements for this are met. The significance values (p-values) that you obtain in the regression are only accurate if the prerequisites for the regression are met. If the requirements are not met, the correct interpretation of the p-values may be problematic.
In...Read More

You need to do a linear regression for your paper but you are not sure how to interpret the results correctly? In this article we explain how to interpret the coefficients of a linear regression correctly.
We assume that you already know how to perform a linear regression. If not, we will be happy to help you.
We use...Read More