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Home/Multiple Linear Regression

Category: Multiple Linear Regression

Multiple Linear Regression

Assumption of Residual Normality in Regression Analysis

By Kanda Data / Date May 06.2024

The assumption of residual normality in regression analysis is a crucial foundation that must be met to ensure the attainment of the Best Linear Unbiased Estimator (BLUE). However, often, many researchers face difficulties in understanding this concept thoroughly.

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Multiple Linear Regression

Can regression estimation coefficients have negative values?

By Kanda Data / Date Apr 29.2024

In regression analysis, estimation coefficients are parameters used to understand the influence of independent variables on the dependent variable. However, an interesting question arises: Can regression estimation coefficients have negative values? In this article, Kanda Data will delve into this phenomenon and discuss its practical implications in linear regression analysis using the ordinary least squares method.

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Multiple Linear Regression

Understanding the Difference between Residual and Error in Regression Analysis

By Kanda Data / Date Apr 05.2024

When expressing a linear regression equation, the terms residual or error often appear at the end of the equation. But what exactly do residual and error mean? And what is the fundamental difference between the two?

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Multiple Linear Regression

Understanding the Importance of the Coefficient of Determination in Linear Regression Analysis

By Kanda Data / Date Mar 21.2024

In linear regression analysis, one important parameter often encountered is the coefficient of determination. The value of this coefficient provides an indication of how well the linear regression model can explain the variation in the data.

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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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Multiple Linear Regression

Understanding the Difference Between R-squared and Adjusted R-squared in OLS Linear Regression Output

By Kanda Data / Date Jan 21.2024

R-squared (R²) and Adjusted R-squared (R² adjusted) are key metrics frequently used to assess the effectiveness of a linear regression model. The R-squared value provides information about the proportion of variability in the dependent variable explained by the independent variable in the linear regression equation.

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Multiple Linear Regression

Choosing the Right Variables in Linear Regression using the OLS Method

By Kanda Data / Date Oct 25.2023

Linear regression analysis is frequently employed by researchers to investigate the impact of independent variables on dependent variables. The Ordinary Least Squares (OLS) method is a popular choice among scholars for estimating parameters in linear regression models. The OLS technique aims to minimize the squared differences between observed and predicted values.

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