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Tag: multiple linear regression

Regression Tutorial using Excel

How to Perform Multiple Linear Regression in Excel | Data Analysis Toolpak Tutorial

By Kanda Data / Date Jun 29.2024

Multiple linear regression is a statistical method used to analyze two or more independent variables in relation to a dependent variable. In this article, Kanda Data will discuss how to perform multiple linear regression analysis using Excel through the Analysis Toolpak menu.

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Statistics

Dummy Variables in Multiple Linear Regression Analysis with the OLS Method

By Kanda Data / Date Jun 02.2024

Multiple linear regression analysis is a well-known technique frequently used by researchers to analyze the influence of independent variables on dependent variables. The ordinary least squares (OLS) method is one of the most commonly used methods in this analysis.

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Multiple Linear Regression

Multicollinearity Test in Multiple Linear Regression Analysis

By Kanda Data / Date May 09.2024

In multiple linear regression analysis, there is an assumption that the model constructed is not affected by multicollinearity issues, where two or more independent variables are strongly correlated. Multicollinearity can lead to errors in parameter estimation and reduce the reliability of the model.

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Multiple Linear Regression

How to Determine the F-Table Value (F Critical Value) in Excel

By Kanda Data / Date Feb 09.2024

In assessing the fit of a linear regression model, researchers need to find the critical values from the F-distribution (F-table). Typically, researchers often use these tables to evaluate the results of regression analysis. However, with technological advancements, determining the F-table value can easily be obtained using Excel.

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Multiple Linear Regression

How to Determine the T-table (T critical value) in Excel for Linear Regression Analysis

By Kanda Data / Date Feb 07.2024

In linear regression analysis, to determine the significance of the regression coefficients, researchers need to find the critical values from the t-student distribution (T-table). Typically, researchers often use these tables to evaluate the results of regression analysis. However, with technological advancements, determining the T-table value can easily be obtained using a spreadsheet, such as Excel.

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Econometrics

Assumptions Required in Multiple Linear Regression Analysis Using Ordinary Least Squares (OLS) Method

By Kanda Data / Date Sep 26.2023

Multiple linear regression with the Ordinary Least Squares (OLS) method is one of the statistical techniques used to assess the influence of two or more independent variables on a dependent variable. The OLS method is carried out by minimizing the sum of squared errors between the model’s predictions and the actual values of the dependent variable.

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Assumptions of Linear Regression

Understanding Normality Test in Ordinary Least Squares Linear Regression

By Kanda Data / Date Sep 19.2023

Linear regression analysis examines the influence of independent variables on dependent variables. This analysis can take the form of simple linear regression or multiple linear regression. Most linear regression analyses utilize the Ordinary Least Squares (OLS) method.

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Multiple Linear Regression

How to Detect Multicollinearity in Multiple Linear Regression Equations Using the OLS Method

By Kanda Data / Date Sep 15.2023

Multicollinearity testing is one of the assumptions in the least squares method of multiple linear regression. This test is conducted to determine whether there is a strong correlation between independent variables.

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