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About Assumptions in Regression Model PDF
This document summarizes key points from a lecture on assumptions in regression modeling: 1) The standard regression model makes four key assumptions: independent observations with constant variance that are normally distributed around the regression line. 2) Checking the assumptions involves examining residuals, which represent the unexplained variation from the regression model. Common diagnostics include scatterplots of residuals and added variable plots. 3) Violations of the assumptions, like non-linearity, autocorrelation, heteroscedasticity, or non-normality, can be detected graphically with residual plots, added variable plots, and quantile plots. Transformations may be needed to satisfy the assumptions.
Detailed Information
| Author: | ['testtt'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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