Regression Diagnostics

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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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catalog / Data Science
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Look inside
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.

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

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