## Session Overview

This lecture is about how to use computation to help understand experimental data. It talks about using linear regression to fit a curve to data, and introduces the coefficient of determination as a measure of the tightness of a fit. |

## Session Activities

### Lecture Videos

## About this Video

Topics covered: Arrays, curve fitting, numpy, pylab, least squares fit, prediction.

## Resources

- Lecture code handout (PDF)
- Lecture code (PY)
- Lecture slides (PDF)
- Lecture data files (ZIP) (This ZIP file contains: 3 .txt files.)

### Recitation Videos

## About this Video

Topics covered: Data distributions, mean, standard deviation, Monte Carlo simulations, Monty Hall problem, Riemann sum method, data regressions, r^2 (r-squared), coefficient of termination, scientific applications of programming.

## Check Yourself

What is an objective function?

› *View/hide answer*

One that provides a quantitative assessment of how well the curve fits the data.

What method of curve fitting is used by polyfit?

› *View/hide answer*

Linear regression.

What does curve fitting do?

› *View/hide answer*

Relates an independent variable to an estimated value of a dependent variable.

What is the coefficient of determination?

› *View/hide answer*

Coefficient of determination, R^2, is equal to 1 – (estimated error)/(variance of the actual data)