Tag: multiple linear regression
How to Create Dummy Variables in Multiple Linear Regression Analysis
For those of you conducting multiple linear regression analysis, have you ever used dummy variables? These variables are very useful when we want to include categorical variables in a multiple linear regression equation.
How to Detect Normally Distributed Data in Linear Regression Analysis
When you conduct data analysis using linear regression, there are several assumptions that must be met. We need to fulfill these assumptions to ensure that the estimation results are consistent and unbiased.
How to Automatically Display Residual Values in Regression Analysis Using Excel
Residual values play an important role in linear regression analysis. These residuals are used for OLS assumption tests, such as normality tests and heteroskedasticity tests. For instance, one of the key assumptions in linear regression analysis is that the residuals are normally distributed.
How to Calculate Tolerance Value and Variance Inflation Factor (VIF)
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.
How to Calculate the Variance Inflation Factor (VIF) in a Multicollinearity Test for 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.
How to Perform Multiple Linear Regression Analysis in Excel: Data Analysis Tools
Multiple linear regression analysis is a method used when a researcher aims to estimate the effect of independent variables on a dependent variable. In multiple linear regression, the number of independent variables must be at least two.
How to Analyze Heteroskedasticity for Time Series Data in Multiple Linear Regression and Its Interpretation
The heteroskedasticity test is one of the assumption tests in the Ordinary Least Squares (OLS) linear regression method, aimed at ensuring that the residual variance remains constant. If the multiple linear regression equation being tested shows non-constant residual variance, this is referred to as heteroskedasticity.
Tutorial on R Studio: Testing Residual Normality in Multiple Linear Regression for Time Series Data
The normality test in multiple linear regression analysis is aimed at detecting whether the residuals are normally distributed. In research using time series data, it is also necessary to perform a normality test to ensure that the required assumptions are met.