Tag: econometrics
How to Create Dummy Variables in Multiple Linear Regression Analysis
For those of you conducting multiple linear regression analysis, have you ever used dummy variables? These variables are very useful when we want to include categorical variables in a multiple linear regression equation.
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.
Understanding Cross-Section, Time Series, and Panel Data Structures in Research
For those of you currently conducting research, I believe it’s important to have a solid understanding of data structure before starting. This is crucial because the structure of your data will determine the appropriate analytical tools to use when analyzing your research results.
Alternative to One-Way ANOVA When Data Are Not Normally Distributed
If you’re conducting research to compare the means of more than two sample groups, one-way ANOVA is a commonly used statistical test. However, using this test comes with certain assumptions that must be met, specifically, that the data are normally distributed and homogenous.
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.
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.
Differences in Assumptions of Normality, Heteroscedasticity, and Multicollinearity in Linear Regression Analysis
If you analyze research data using linear regression, it is crucial to understand the required assumptions. Understanding these assumption tests is essential to ensure consistent and unbiased analysis results.
How to Analyze Multicollinearity in Linear Regression Using R Studio
In linear regression analysis using the Ordinary Least Square method, it is necessary to ensure that there is no strong correlation between independent variables. To obtain the best linear unbiased estimator, there must not be a strong correlation between the independent variables.