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Home/Data Analysis in R

Category: Data Analysis in R

How to Perform Multiple Linear Regression Analysis on Time Series Data Using R Studio

By Kanda Data / Date Oct 07.2024 / Category Data Analysis in R

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.

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How to Perform Multiple Linear Regression Analysis Using R Studio: A Complete Guide

By Kanda Data / Date Sep 30.2024 / Category Data Analysis in R

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.

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Data Analysis in R

Testing and Interpreting Homoscedasticity in Simple Linear Regression with R Studio

By Kanda Data / Date Dec 16.2023

Homoscedasticity is a crucial assumption in ordinary least square (OLS) linear regression analysis. This assumption refers to the consistent variability of regression residuals across all predictor values. Homoscedasticity assumes that the spread of residual regression errors remains relatively constant along the regression line.

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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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Data Analysis in R

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

By Kanda Data / Date Dec 04.2023

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 decisions and minimize uncertainty.

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Data Analysis in R

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

By Kanda Data / Date Nov 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.

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Data Analysis in R

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

By Kanda Data / Date May 07.2023

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.

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Data Analysis in R

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

By Kanda Data / Date Apr 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.

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Categories

  • Article Publication
  • Assumptions of Linear Regression
  • Comparison Test
  • Correlation Test
  • Data Analysis in R
  • Econometrics
  • Excel Tutorial for Statistics
  • Multiple Linear Regression
  • Nonparametric Statistics
  • Profit Analysis
  • Regression Tutorial using Excel
  • Research Methodology
  • Simple Linear Regression
  • Statistics

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