Tag: Regression Assumptions
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 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.
How to Analyze Heteroskedasticity in Linear Regression Using R Studio
Heteroskedasticity testing is an assumption test in linear regression using the OLS method to ensure that the residual variance is constant. A constant residual variance is referred to as homoskedasticity.
How to Perform Residual Normality Analysis in Linear Regression Using R Studio and Interpret the Results
Residual normality testing is a key assumption check in linear regression analysis using the Ordinary Least Squares (OLS) method. One essential requirement of linear regression is that the residuals should follow a normal distribution. In this article, Kanda Data shares a tutorial on how to perform residual normality analysis in linear regression using R Studio, along with steps to interpret the results.
Assumptions of Multiple Linear Regression on Cross-Section Data
Multiple linear regression is a statistical technique used to predict the value of a dependent variable based on several independent variables. This regression provides a way to understand and measure the influence of independent variables on the dependent variable.
Assumptions of Multiple Linear Regression on Time Series Data
Multiple linear regression is a statistical analysis technique used to model the relationship between one dependent variable and two or more independent variables. The multiple linear regression model is used to predict the value of the dependent variable based on the estimated values of the independent variables.
Linear Regression Residual Calculation Formula
In linear regression analysis, testing residuals is a very common practice. One crucial assumption in linear regression using the least squares method is that the residuals must be normally distributed.
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.