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About Lecture 4: Simple Linear Regression Models, With Hints at Their Estimation
This document provides an overview of simple linear regression models and the estimation of their parameters. It discusses two approaches: 1) "plug-in" estimates that estimate parameters based on sample statistics approximating population parameters; and 2) least squares estimates that minimize the mean squared error between the model and data. It shows that the least squares estimates are equivalent to the plug-in estimates for simple linear regression. It then derives the bias and variance of the slope parameter estimate β1 and shows it is an unbiased estimator of the true slope.
Detailed Information
| Author: | ['S'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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