# econometrics

## Assumptions Required in Multiple Linear Regression Analysis Using Ordinary Least Squares (OLS) Method

Multiple linear regression with the Ordinary Least Squares (OLS) method is one of the statistical techniques used to assess the influence of two or more independent variables on a dependent variable. The OLS method is carried out by minimizing the sum of squared errors between the model’s predictions and the actual values of the dependent …

## Understanding Normality Test in Ordinary Least Squares Linear Regression

Linear regression analysis examines the influence of independent variables on dependent variables. This analysis can take the form of simple linear regression or multiple linear regression. Most linear regression analyses utilize the Ordinary Least Squares (OLS) method.

## Understanding the t-test for non-normally distributed data

For researchers aiming to explore the differences between two sample groups, the t-test is a viable option. According to theory, the t-test can determine differences between two sample groups, whether they are paired or independent.

## 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.

## Definition and Purpose of Determining Residual Values in Linear Regression Analysis

In linear regression analysis, residual values play a crucial role. The residual value is the difference between the actual and predicted Y values. The actual Y value can be obtained from observations or samples of the dependent variable.