Tag: normality test
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.
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).
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 Perform an Independent Sample t-Test and Interpret the Results in R Studio
The independent sample t-test in R Studio is used to compare two independent groups. Through this t-test, we can determine whether there is a significant difference between the means of the two groups being compared.
Linear Regression Residual Calculation Formula
In linear regression analysis, testing residuals is a very common practice. One crucial assumption in linear regression using the least squares method is that the residuals must be normally distributed.
Assumption of Residual Normality in Regression Analysis
The assumption of residual normality in regression analysis is a crucial foundation that must be met to ensure the attainment of the Best Linear Unbiased Estimator (BLUE). However, often, many researchers face difficulties in understanding this concept thoroughly.
Understanding the Essence of Assumption Testing in Linear Regression Analysis: Prominent Differences between Cross-Sectional Data and Time Series Data
Linear regression analysis has become one of the primary tools for researchers to explore the influence of independent variables on dependent variables. The Ordinary Least Squares (OLS) method has been a mainstay in conducting this linear regression analysis.