Residual Plots
Residual plots are graphical representations of the residuals against the predictor variables in a regression analysis. These plots help assess the assumptions and adequacy of the regression model.
In residual plots, if the residuals exhibit a random pattern around the horizontal axis, it indicates that the regression model is appropriate and adequately captures the variability in the data. However, if the residuals show a systematic pattern, such as a curve or funnel shape, it suggests that the regression model may not be the best fit for the data.
Residual plots also help identify outliers or influential data points that may disproportionately affect the regression analysis results. By examining residual plots, statisticians can make informed decisions about the validity and reliability of the regression model and make any necessary adjustments to improve its accuracy.
Residual Analysis
Residual analysis is a powerful statistical technique used to assess the accuracy of regression models. By examining the differences between observed and predicted values, residual analysis provides information about the adequacy of the model fit. Researchers and analysts need this technique to make better decisions about the validity and reliability of their statistical models.
In this article, we will learn about Residual Analysis in detail.
Table of Content
- What is Residual Analysis?
- Residuals in Regression Analysis
- Residual Plots
- Types of Residual Plots
- ANOVA Residuals
- Residual Plot Analysis
- Assumptions Regarding Residuals in Linear Regression
- Software for Calculating Residual Analysis