Tag: regression
Interpretation of Negative Estimated Coefficients: A Case Study of the Effect of Price on Demand
Introduction
When we conduct regression analysis, it does not always produce positive estimated coefficients. In regression analysis, we often find estimated coefficients that are negative. Not infrequently, this makes us wonder: is this safe for my research?
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.
The Difference Between Residual and Error in Statistics
For those of you who are learning statistics, you’ve probably come across theories explaining the concepts of residual and error. At first glance, they seem almost identical, and many people even think they mean the same thing. However, in statistics, residual and error actually have different meanings.
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.
Assumption Tests in Linear Regression Using Survey Data
The most commonly used linear regression analysis by researchers is the Ordinary Least Squares (OLS) method. However, when applying linear regression with the OLS method, several assumptions must be met to ensure that the estimation results are consistent and unbiased.
What Is a Residual Value in Statistics?
If you’re working with data analysis using linear regression, especially the Ordinary Least Squares (OLS) method, it’s important to understand what a residual is. Why does this matter? Because several assumption tests in OLS regression rely heavily on residual values. That’s why you need a solid understanding of what residuals are and how to calculate them.
Normality Test in Regression: Should We Test the Raw Data or the Residuals?
When we choose to analyze data using linear regression with the OLS method, there are several assumptions that must be met. These assumptions are essential to ensure that the estimation results are consistent and unbiased. This is what we refer to as the Best Linear Unbiased Estimator (BLUE).
How to Perform Multiple Linear Regression Analysis on Time Series Data Using R Studio
Multiple linear regression analysis on time series data, along with its assumption tests, can be performed using R Studio. In a previous article, I explained how to conduct multiple linear regression analysis and assumption tests for cross-sectional data.