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Home/Data Analysis in R

Category: Data Analysis in R

Multicollinearity Test in R Studio for Multiple Linear Regression Using Time Series Data

By Kanda Data / Date Dec 23.2024 / Category Data Analysis in R

In time series data analyzed using multiple linear regression with the ordinary least squares (OLS) method, it is also necessary to test for multicollinearity. The multicollinearity test is one of the assumption tests to ensure the best linear unbiased estimator.

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How to Analyze Heteroskedasticity for Time Series Data in Multiple Linear Regression and Its Interpretation

By Kanda Data / Date Dec 14.2024 / Category Data Analysis in R

The heteroskedasticity test is one of the assumption tests in the Ordinary Least Squares (OLS) linear regression method, aimed at ensuring that the residual variance remains constant. If the multiple linear regression equation being tested shows non-constant residual variance, this is referred to as heteroskedasticity.

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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 Multicollinearity in Linear Regression Using R Studio

By Kanda Data / Date Nov 25.2024 / Category Data Analysis in R

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.

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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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How to Perform an Independent Sample t-Test and Interpret the Results in R Studio

By Kanda Data / Date Oct 21.2024 / Category Data Analysis in R

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.

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How to Perform Paired Sample t-Tests in R Studio and Interpret the Results

By Kanda Data / Date Oct 14.2024 / Category Data Analysis in R

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.

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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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