Category: Econometrics
When Should Natural Logarithmic Data Transformation Be Applied?
When researchers, practitioners, or students are conducting data analysis on research results, they are often faced with data that do not meet the assumptions required by the chosen analytical method. After testing, it may turn out that the data distribution is highly skewed, the variance is not constant, or non-linear relationships between variables are observed. These conditions represent common challenges in statistical analysis, especially when using parametric methods such as linear regression analysis.
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.
Natural Logarithm Data Transformation to Improve Data Normality, Is It True?
In parametric statistical analysis, several assumptions must be met, one of which is the assumption that data should be normally distributed. However, in practice, the data obtained from research does not always follow a normal distribution based on statistical tests. Therefore, some researchers attempt to adjust the distribution of data to make it more closely resemble a normal distribution. One common method is data transformation. Among various types of data transformations, the natural logarithm transformation is one of the most commonly used.
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?
How to Find Residuals Using the Data Analysis ToolPak in Excel
Residuals are the differences between the observed values of the dependent variable and the predicted values from the dependent variable. Residuals are an important measure in inferential analysis, particularly in regression analysis. Given the importance of residuals, we will discuss how to find residual values using Excel.
The Difference Between Simultaneous Equation System Model and Linear Regression Equation
We might all be familiar with linear regression equations, but how many of us have delved deeper into the simultaneous equation system model? It’s worth noting that the simultaneous equation system model is far more complex than linear regression equations.
Can Data Transformation Be Done More Than Once?
For those of us accustomed to conducting research, understanding how to analyze data is a crucial skill to master. In the process, when we are processing data, we are sometimes faced with the choice of data transformation.