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Tag: multiple linear regression

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 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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Assumption Tests for Multiple Linear Regression on Cross-Sectional Data

By Kanda Data / Date Sep 16.2024 / Category Assumptions of Linear Regression

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 some respects.

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Regression Analysis on Non-Parametric Dependent Variables: Is It Possible?

By Kanda Data / Date Aug 28.2024 / Category Econometrics

In multiple linear regression analysis, the measurement scale of the dependent variable is typically parametric. However, can multiple linear regression analysis be applied to a dependent variable measured on a nominal (non-parametric) scale?

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Understanding the Differences in Using R Squared and Adjusted R Squared in Research

By Kanda Data / Date Aug 27.2024 / Category Multiple Linear Regression

When you choose to use linear regression analysis, it’s essential to master and understand the interpretation of the coefficient of determination. The coefficient of determination is one of the key indicators in linear regression analysis that can be used as a metric to determine the goodness of fit of a regression model.

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Assumptions of Linear Regression

Assumptions of Multiple Linear Regression on Cross-Section Data

By Kanda Data / Date Jul 29.2024

Multiple linear regression is a statistical technique used to predict the value of a dependent variable based on several independent variables. This regression provides a way to understand and measure the influence of independent variables on the dependent variable.

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Assumptions of Linear Regression

Assumptions of Multiple Linear Regression on Time Series Data

By Kanda Data / Date Jul 25.2024

Multiple linear regression is a statistical analysis technique used to model the relationship between one dependent variable and two or more independent variables. The multiple linear regression model is used to predict the value of the dependent variable based on the estimated values of the independent variables.

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