Data Analysis in R Archives - Page 2 of 3

Simple Linear Regression Analysis Using R Studio and How to Interpret It

Data Analysis in R / By Kanda Data / December 4, 2023

In the real world, accurate decisions need to be based on a deep understanding of data. One tool for processing and elaborating data is simple linear regression analysis. Simple linear regression analysis allows us to read patterns among scattered data points. A correct understanding of regression analysis gives us the power to make more accurate …

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How to Test for Normality in Linear Regression Analysis Using R Studio

Data Analysis in R / By Kanda Data / November 21, 2023

Testing for normality in linear regression analysis is a crucial part of inferential method assumptions, requiring regression residuals to be normally distributed. Residuals are the differences between observed values and those predicted by the linear regression model.

How to Test Normality of Residuals in Linear Regression and Interpretation in R (Part 4)

Data Analysis in R / By Kanda Data / May 7, 2023

The normality test of residuals is one of the assumptions required in the multiple linear regression analysis using the ordinary least square (OLS) method. The normality test of residuals is aimed to ensure that the residuals are normally distributed.

How to Test Heteroscedasticity in Linear Regression and Interpretation in R (Part 3)

Data Analysis in R / By Kanda Data / April 30, 2023

One of the assumptions required in Ordinary Least Squares (OLS) linear regression is that the variance of the residuals is constant. This assumption is often referred to as the homoscedasticity assumption. Some researchers are more familiar with the term heteroscedasticity test.

How to Analyze Multicollinearity in Linear Regression and its Interpretation in R (Part 2)

Data Analysis in R / By Kanda Data / April 17, 2023

Non-multicollinearity is one of the assumptions required in the ordinary least square (OLS) method of linear regression analysis. Non-multicollinearity assumption implies that there is no strong correlation among the independent variables in the equation.