Category: Multiple Linear Regression
Multicollinearity Test in Multiple Linear Regression Analysis
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.
Assumption of Residual Normality in Regression Analysis
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.
Can regression estimation coefficients have negative values?
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.
Understanding the Difference between Residual and Error in Regression Analysis
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?
Understanding the Importance of the Coefficient of Determination in Linear Regression Analysis
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.
How to Determine the F-Table Value (F Critical Value) in Excel
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.
How to Determine the T-table (T critical value) in Excel for Linear Regression Analysis
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.
Understanding the Difference Between R-squared and Adjusted R-squared in OLS Linear Regression Output
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.