Tag: linear regression analysis
Understanding the Difference Between R-squared and Adjusted R-squared in OLS Linear Regression Output
R-squared (R²) and Adjusted R-squared (R² adjusted) are key metrics frequently used to assess the effectiveness of a linear regression model. The R-squared value provides information about the proportion of variability in the dependent variable explained by the independent variable in the linear regression equation.
Simple Linear Regression Analysis Using R Studio and How to Interpret It
In the real world, accurate decisions need to be based on a deep understanding of data. One tool for processing and elaborating data is simple linear regression analysis. Simple linear regression analysis allows us to read patterns among scattered data points. A correct understanding of regression analysis gives us the power to make more accurate decisions and minimize uncertainty.
How to Test for Normality in Linear Regression Analysis Using R Studio
Testing for normality in linear regression analysis is a crucial part of inferential method assumptions, requiring regression residuals to be normally distributed. Residuals are the differences between observed values and those predicted by the linear regression model.
How to Perform Linear Regression Analysis Using Excel | A Complete Step-by-Step Tutorial and Guide
In today’s post, Kanda Data provides a comprehensive tutorial on how to perform linear regression analysis using Microsoft Excel. Before we dive into the tutorial, the first step is to activate the Analysis ToolPak in Excel. Next, Kanda Data presents a case study example of research data to be analyzed using linear regression analysis.
Definition and Purpose of Determining Residual Values in Linear Regression Analysis
In linear regression analysis, residual values play a crucial role. The residual value is the difference between the actual and predicted Y values. The actual Y value can be obtained from observations or samples of the dependent variable.
How to Choose Regression, Correlation, or Difference Test for Variable Association Analysis
Selecting the appropriate analysis method will prevent errors in concluding research results. There are various methods of data analysis that researchers can choose from. The selection of data analysis methods depends on the research objectives and the characteristics of the collected data.