Author: Kanda Data
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.
Can Outliers Make Your Data Look Non-Normal? Here’s a Simulation and How to Handle It
In many parametric statistical tests, it’s assumed that the data must follow a normal distribution. That’s why, when we’ve gathered research data and are planning to use parametric statistical analysis, checking for normality is crucial. We need to make sure that the data follows a normal distribution before proceeding with further analysis.
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.
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 Find the T-Table Value for Regression Using Excel
In linear regression analysis, we often want to determine whether the independent variable truly influences the dependent variable. Therefore, linear regression analysis is one type of associative test that aims to determine how the independent variable affects the dependent variable.
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.