What is Residual Analysis?
Residual analysis is a statistical technique used to assess the goodness of fit of a statistical model. It involves examining the differences between observed data points and the values predicted by the model. These differences, known as residuals, provide insights into how well the model captures the underlying patterns in the data.
One way to understand residual analysis is by examining the components of a residual plot:
Component | Description |
---|---|
Residuals | Differences between observed and predicted values. |
Residual Plot | Graphical representation of residuals against predictor values. |
Patterns | Presence of patterns in residual plots indicates model inadequacy or outliers. |
Residual analysis helps identify potential issues with the statistical model, such as outliers or violations of assumptions.
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