Tag: Regression Analysis
Regression Analysis for Binary Categorical Dependent Variables
When we talk about regression analysis, we often think about parametric variables measured on at least an interval or ratio scale. But what if we want to analyze the effect of independent variables on a dependent variable that happens to be categorical in nature?
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.
The Impact of Residual Variance on P-Value in Regression Analysis
When conducting linear regression analysis on your research data, you naturally hope that some independent variables significantly affect the dependent variable. Achieving this indicates that you’ve successfully selected independent variables that are presumed to influence the dependent variable.
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 Find Residuals Using the Data Analysis ToolPak in Excel
Residuals are the differences between the observed values of the dependent variable and the predicted values from the dependent variable. Residuals are an important measure in inferential analysis, particularly in regression analysis. Given the importance of residuals, we will discuss how to find residual values using Excel.
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.