Category: Multiple Linear Regression
How to Detect Multicollinearity in Multiple Linear Regression Equations Using the OLS Method
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.
How to Interpret Dummy Variables in Ordinary Least Squares Linear Regression Analysis
Dummy variables, which have non-parametric measurement scales, can be used in specifying linear regression equations. The linear regression equation I’m referring to here is the ordinary least squares (OLS) method. As we already know, most variables are measured on interval and ratio scales in ordinary least squares linear regression equations.
How to Interpret Linear Regression Analysis Output | R Squared, F Statistics, and T Statistics
Once the researcher has successfully conducted linear regression analysis, the next step is to interpret the results. It is crucial for the researcher to possess sufficient knowledge to interpret the findings. The interpretation based on these results can be used to draw conclusions from the research.
How to Interpret Negative Coefficient Estimations in Linear Regression?
The ordinary least squares (OLS) method is commonly employed in linear regression analysis to establish the relationship between the independent and dependent variables. Despite its numerous advantages, researchers must meet certain requirements to use this method.
How to Perform Multiple Linear Regression using Data Analysis in Excel
Researchers have widely used linear regression analysis to analyze a phenomenon. The regression analysis is intended to determine the effect of independent variables on the dependent variable. Researchers use linear regression analysis, and practitioners in the industry also often use linear regression analysis. The output of linear regression analysis can be used for consideration in making business decisions in a company.
Formula to Calculate Analysis of Variance (ANOVA) in Regression Analysis
In regression analysis, both simple linear regression and multiple linear regression, it is necessary to conduct an analysis of variance calculation to find the statistical F value. The table for the analysis of variance in the regression analysis is called the ANOVA table.
How to Find Residual Value in Multiple Linear Regression using Excel
Residual values in linear regression analysis can be used to test for normality. In addition, the heteroscedasticity test also requires the variance value of the residual. Therefore, the residual value is important in the linear regression assumption test with the Ordinary Least Square (OLS) method.
How to Analyze Multiple Linear Regression in Excel and Interpret the Output
Researchers often use linear regression analysis to analyze associative relationships between variables. Multiple linear regression is an analysis that researchers often use because it can analyze the effect of more than two independent variables on the dependent variable.