In linear regression analysis using the Ordinary Least Square method, it is necessary to ensure that there is no strong correlation between independent variables. To obtain the best linear unbiased estimator, there must not be a strong correlation between the independent variables.
Heteroskedasticity testing is an assumption test in linear regression using the OLS method to ensure that the residual variance is constant. A constant residual variance is referred to as homoskedasticity.
Residual normality testing is a key assumption check in linear regression analysis using the Ordinary Least Squares (OLS) method. One essential requirement of linear regression is that the residuals should follow a normal distribution. In this article, Kanda Data shares a tutorial on how to perform residual normality analysis in linear regression using R Studio, …
The independent sample t-test in R Studio is used to compare two independent groups. Through this t-test, we can determine whether there is a significant difference between the means of the two groups being compared.
Paired sample t-tests, which aim to identify differences between two paired data sets, can be analyzed using R Studio. Through paired sample t-tests, we can determine whether there are significant changes after a certain treatment or program carried out during the research activity.
