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Regression Analysis

Regression Diagnostics

This project is part of the liveProject series Regression for Life Expectancy Analysis.
prerequisites
intermediate Python • basics of statistics (regression analysis) • basics of NumPy and pandas
skills learned
checking the assumptions of the regression model • programming "QQ-plots," "Residuals vs. Fitted" plots, and "Scale-Location" plots • visualizing relationships and distributions with seaborn
Monica Guimaraes
1 week · 8-10 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Regression for Life Expectancy Analysis. liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%) self-paced learning
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In this liveProject, you’ll build your own Python data library to check three of the assumptions of the regression model: normality, linearity, and constant variance. Confidence in your regression models depends on how well you have satisfied these assumptions. Once you’ve developed functions and plots that can check these assumptions, you’ll master techniques for correcting them. With this library, you will expand your data science toolbox with important diagnostics tools that will allow you to be confident in your results.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Monica Guimaraes
Monica Guimaraes has more than fifteen years of experience organizing and teaching undergraduate courses in automata theory, numerical analysis, programming languages, and statistics. Her passion for artificial intelligence goes back to her college years, having done her thesis in automatic-theorem-proving and working later as an AI researcher. She also has extensive experience in software development in corporations.

prerequisites

This liveProject is for confident Python programmers. To begin this liveProject you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basics of pandas
  • Basics of NumPy
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of Python data analysis
  • Regression Analysis with scikit-Learn/statsmodels

you will learn

In this liveProject, you’ll learn vital skills to test the validity of your regression results. These skills are easy to transfer to any regression analysis.

  • Checking the assumptions of the regression model
  • Programming "QQ-plots," "Residuals vs. Fitted" plots, and "Scale-Location" plots
  • Visualizing relationships and distributions with seaborn

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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