## Regression Basics

Versión 2.0.3.0 (309 KB) por
Interactive courseware module that addresses the fundamentals of regression analysis taught in STEM courses.

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# Regression Basics

or

Curriculum Module
Created with R2020a. Compatible with R2020a and later releases.

## Description

This package contains a live script and supporting files to illustrate some basics of regression analysis. The materials are designed to be flexible and can be easily modified to accommodate a variety of teaching and learning methods. We include a brief background, interactive illustrations, tasks, reflection questions, a real-world application example, and a guided exercise for the different concepts explored.

The instructions inside the live scripts will guide you through the tasks and activities one section at a time. To run this interactive script in a controls-only mode, use the Hide code button on the View tab of the MATLAB toolstrip.

Learning Goals

• Explain the difference between linear, multiple linear, and nonlinear regression.
• Use ordinary least squares to analytically solve for linear regression parameters.
• Assess and improve the performance of a regression model using a goodness-of-fit measure.
• Apply gradient descent to iteratively minimize a cost function and estimate model parameters.
• Explain the effect of increasing and decreasing the learning rate and number of steps for the gradient descent algorithm.
• Use a linear regression model to perform short-term forecasting.

## Suggested Prework

MATLAB Onramp – a free two-hour introductory tutorial to learn the essentials of MATLAB®.

## Details

`regressionBasics.mlx` An interactive lesson that introduces the fundamentals of regression analysis. Students apply a basic linear regression to model real-world electricity load data.

`electricityLoadData.mlx` A supplementary script to download the external electricity load data from New York ISO for use in the practice problem.

`regressSolnIm/`
This folder includes supplementary image files containing solutions for tasks in `regressionBasics.mlx`. The main script provides controls to hide or expose the solutions when needed. Ensure that this folder is in the same location as `regressionBasics.mlx`

`linearData.mat`, `linearData2.mat`, `multivariateData.mat`, `nonlinearData.mat`
Data files containing some sample data for the different types of regression problems.

## Products

MATLAB®, Statistics and Machine Learning Toolbox™

The license for this module is available in the LICENSE.TXT file in this GitHub repository.

## Support

Have any questions or feedback? Contact the MathWorks online teaching team.

### Citar como

Emma Smith Zbarsky (2023). Regression Basics (https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v2.0.3), GitHub. Recuperado .

##### Compatibilidad con la versión de MATLAB
Se creó con R2020a
Compatible con cualquier versión desde R2020a
Windows macOS Linux

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Versión Publicado Notas de la versión
2.0.3.0

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v2.0.3

2.0.2

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v2.0.2

2.0.1

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v2.0.1

2.0.0

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v2.0.0

1.2.0

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v1.2.0

1.1.1

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Regression-Basics/releases/tag/v1.1.1

1.1.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.