# Reading 12: Regression I: Introduction, Linear Models, Regularization#

*For the class on Wednesday, March 13th*

## Reading assignments#

Read the following sections of [ICVG20]:

Chap. 8 “Regression and Model Fitting”

Lead paragraphs (page 311)

Sec. 8.1 “Formulation of the Regression Problem”

Sec. 8.2 “Regression for Linear Models”

Sec. 8.3 “Regularization and Penalizing the Likelihood”

Sec.

**8.8**“Uncertainties in the Data”

## Questions#

Hint

Submit your answer on Canvas. Due at noon, Wednesday, March 13th.

List anything from your reading that confuses you. Explain why they confuse you. If nothing confuses you, briefly summarize what you have learned from this reading assignment.

Use your own words to define “linear regression”. Can you use linear regression to fit a parabola to a set of data points? Why or why not.

## Discussion Preview#

Note

We will discuss the following questions in class. They are included here so that you have a chance to think about them before class.
You need *not* submit your answers as part of this assignment.

What problems in physics and astronomy are suitable for linear regression? Give some specific examples.

How to understand regression in a probabilistic view? In particular, what is the difference between regression and Bayesian inference?