Tag: econometrics
Assumption Tests for Multiple Linear Regression on Cross-Sectional Data
In multiple linear regression analysis using cross-sectional data, there are several assumption tests that must be conducted to obtain the best linear unbiased estimator. It is crucial to understand which assumption tests are required for research utilizing cross-sectional data. This is important because the assumption tests for cross-sectional, time series, and panel data differ in some respects.
Differences Between the Null Hypothesis and the Alternative Hypothesis in Statistical Analysis
Statistical hypotheses, consisting of the null hypothesis and the alternative hypothesis, play a crucial role in the process of testing and analyzing statistical data. Understanding the concept of a hypothesis is a critical first step in ensuring that research results are valid and scientifically accountable.
Regression Analysis on Non-Parametric Dependent Variables: Is It Possible?
In multiple linear regression analysis, the measurement scale of the dependent variable is typically parametric. However, can multiple linear regression analysis be applied to a dependent variable measured on a nominal (non-parametric) scale?
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.
The Difference Between Pearson Correlation and Spearman Rank Correlation in Research
For those currently conducting data analysis, correlation tests are commonly used to measure the relationship between two variables. However, not all correlation tests are suitable for every type of data. As we all know, data types and characteristics can vary.
Dummy Variables in Multiple Linear Regression Analysis with the OLS Method
Multiple linear regression analysis is a well-known technique frequently used by researchers to analyze the influence of independent variables on dependent variables. The ordinary least squares (OLS) method is one of the most commonly used methods in this analysis.
Interpreting Negative Intercept in Regression
When conducting regression analysis, we obtain the intercept and coefficient estimates for each independent variable. These values, both intercept and coefficients, can be positive or negative.
Choosing the Right Variables in Linear Regression using the OLS Method
Linear regression analysis is frequently employed by researchers to investigate the impact of independent variables on dependent variables. The Ordinary Least Squares (OLS) method is a popular choice among scholars for estimating parameters in linear regression models. The OLS technique aims to minimize the squared differences between observed and predicted values.