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.
Multiple linear regression analysis on time series data, along with its assumption tests, can be performed using R Studio. In a previous article, I explained how to conduct multiple linear regression analysis and assumption tests for cross-sectional data.
Multiple linear regression analysis requires commands to be executed in R Studio. Given the importance of understanding how to analyze and interpret multiple linear regression using R Studio, Kanda Data will write an article discussing this topic.
Non-parametric variables measured using the Likert scale (ordinal scale) have a slightly different analysis approach compared to parametric variables. This Likert scale approach is often used in social and management research. In such studies, we are often confronted with variables that cannot be directly quantified.
In multiple linear regression analysis using cross-sectional data, there are several assumption tests that must be conducted to obtain the best linear unbiased estimator. It is crucial to understand which assumption tests are required for research utilizing cross-sectional data. This is important because the assumption tests for cross-sectional, time series, and panel data differ in …
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