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Home/Assumptions of Linear Regression

Category: Assumptions of Linear Regression

Can Outliers Make Your Data Look Non-Normal? Here’s a Simulation and How to Handle It

By Kanda Data / Date Jul 03.2025 / Category Assumptions of Linear Regression

In many parametric statistical tests, it’s assumed that the data must follow a normal distribution. That’s why, when we’ve gathered research data and are planning to use parametric statistical analysis, checking for normality is crucial. We need to make sure that the data follows a normal distribution before proceeding with further analysis.

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Assumption Tests in Linear Regression Using Survey Data

By Kanda Data / Date Jun 16.2025 / Category Assumptions of Linear Regression

The most commonly used linear regression analysis by researchers is the Ordinary Least Squares (OLS) method. However, when applying linear regression with the OLS method, several assumptions must be met to ensure that the estimation results are consistent and unbiased.

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Normality Test in Regression: Should We Test the Raw Data or the Residuals?

By Kanda Data / Date Jun 09.2025 / Category Assumptions of Linear Regression

When we choose to analyze data using linear regression with the OLS method, there are several assumptions that must be met. These assumptions are essential to ensure that the estimation results are consistent and unbiased. This is what we refer to as the Best Linear Unbiased Estimator (BLUE).

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How to Calculate Tolerance Value and Variance Inflation Factor (VIF)

By Kanda Data / Date Feb 10.2025 / Category Assumptions of Linear Regression

The tolerance value and Variance Inflation Factor (VIF) are important metrics that you can use to detect multicollinearity among independent variables. If we recall the basic theory, multicollinearity testing is an assumption test in the Ordinary Least Squares (OLS) regression method, which aims to ensure that there is no strong correlation between independent variables.

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How to Calculate the Variance Inflation Factor (VIF) in a Multicollinearity Test for Regression

By Kanda Data / Date Jan 29.2025 / Category Assumptions of Linear Regression

In linear regression analysis, to obtain the best linear unbiased estimator, you need to perform a series of assumption tests. One of the assumption tests required in linear regression is the multicollinearity test.

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Differences in Assumptions of Normality, Heteroscedasticity, and Multicollinearity in Linear Regression Analysis

By Kanda Data / Date Dec 30.2024 / Category Assumptions of Linear Regression

If you analyze research data using linear regression, it is crucial to understand the required assumptions. Understanding these assumption tests is essential to ensure consistent and unbiased analysis results.

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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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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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