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Home/R Studio Tutorial

Tag: R Studio Tutorial

Tutorial on R Studio: Testing Residual Normality in Multiple Linear Regression for Time Series Data

By Kanda Data / Date Dec 09.2024 / Category Data Analysis in R

The normality test in multiple linear regression analysis is aimed at detecting whether the residuals are normally distributed. In research using time series data, it is also necessary to perform a normality test to ensure that the required assumptions are met.

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How to Analyze Heteroskedasticity in Linear Regression Using R Studio

By Kanda Data / Date Nov 19.2024 / Category Data Analysis in R

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.

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How to Perform Residual Normality Analysis in Linear Regression Using R Studio and Interpret the Results

By Kanda Data / Date Nov 11.2024 / Category Data Analysis in R

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, along with steps to interpret the results.

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Data Analysis in R

How to Conduct a Normality Test in Simple Linear Regression Analysis Using R Studio and How to Interpret the Results

By Kanda Data / Date Dec 10.2023

The Ordinary Least Squares (OLS) method in simple linear regression analysis is a statistical technique aimed at understanding the influence of an independent variable on a dependent variable. In simple linear regression, there is only one dependent variable and one independent variable.

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