Tag: Statistical Analysis
When is autocorrelation testing performed in linear regression analysis?
In regression analysis, researchers must ensure that the constructed model meets several required assumptions. One assumption in ordinary least square linear regression is the absence of autocorrelation in the model’s residuals. Autocorrelation occurs when there is a correlation pattern among the residual values in the regression model.
Understanding the Difference between Residual and Error in Regression Analysis
When expressing a linear regression equation, the terms residual or error often appear at the end of the equation. But what exactly do residual and error mean? And what is the fundamental difference between the two?
Understanding the Essence of the Difference Between Descriptive Statistics and Inferential Statistics in Research
In conducting research, understanding the basic theory of statistics becomes crucial for researchers. Why is this so important? Because to extract accurate conclusions from research data, careful analysis using statistical tools is needed.
How to Analyze Correlation between Ratio and Ordinal Scale Variables (Different Measurement Scales)
In correlation analysis, we often use Pearson correlation to test the relationship between variables measured on a ratio/interval scale. Variables measured on a ratio/interval scale have a greater potential to meet the normality assumption for data testing.
How to Conduct a Normality Test in Simple Linear Regression Analysis Using R Studio and How to Interpret the Results
The Ordinary Least Squares (OLS) method in simple linear regression analysis is a statistical technique aimed at understanding the influence of an independent variable on a dependent variable. In simple linear regression, there is only one dependent variable and one independent variable.
Descriptive Statistics Analysis in Excel: A Step-by-Step Guide for Researcher
Descriptive statistics analysis is a crucial step in research, aimed at summarizing and illustrating fundamental information from a dataset. Through descriptive statistics, we can present data in a more informative and easily understandable format.
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.
Hypothesis Testing: Unveiling Insights in Multiple Linear Regression Analysis
In inferential statistics, we need to formulate research hypotheses. These research hypotheses are formulated according to the research objectives. Furthermore, statistical hypotheses need to be established in the analysis method, consisting of null and alternative hypotheses.