Category: Multiple Linear Regression
Reasons Why the R-Squared Value in Time Series Data Is Higher Than in Cross-Section Data
If you’re doing regression analysis, R-squared is one of the most important metrics you need to understand. R-squared shows how much of the variation in the dependent variable can be explained by the variation in the independent variables in a regression model.
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?
Dummy Variables: A Solution for Categorical Variables in OLS Linear Regression
If you’re analyzing data using OLS linear regression, there are certain assumptions you need to meet. The purpose of these assumption tests is to ensure that the estimation results are consistent and unbiased.
How to Find the T-Table Value for Regression Using Excel
In linear regression analysis, we often want to determine whether the independent variable truly influences the dependent variable. Therefore, linear regression analysis is one type of associative test that aims to determine how the independent variable affects the dependent variable.
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.
Understanding the Differences in Using R Squared and Adjusted R Squared in Research
When you choose to use linear regression analysis, it’s essential to master and understand the interpretation of the coefficient of determination. The coefficient of determination is one of the key indicators in linear regression analysis that can be used as a metric to determine the goodness of fit of a regression model.
How to Correctly Interpret a Negative Estimation Coefficient
The goal of linear regression analysis is to understand the influence of independent variables on dependent variables. The result of linear regression analysis is the regression coefficient, which indicates the size and magnitude of the influence of independent variables on dependent 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.