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InicioPythonIntermediate Regression with statsmodels in Python

Intermediate Regression with statsmodels in Python

Learn to perform linear and logistic regression with multiple explanatory variables.

Comience El Curso Gratis
4 Horas14 Videos52 Ejercicios
9333 AprendicesDeclaración de cumplimiento

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Descripción del curso

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. In this course, you’ll build on the skills you gained in "Introduction to Regression in Python with statsmodels", as you learn about linear and logistic regression with multiple explanatory variables. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, Taiwan house prices and customer churn modeling, and more. By the end of this course, you’ll know how to include multiple explanatory variables in a model, discover how interactions between variables affect predictions, and understand how linear and logistic regression work.
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1. 1

Parallel Slopes

Gratuito

Extend your linear regression skills to parallel slopes regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

Reproducir Capítulo Ahora
Parallel slopes linear regression
50 xp
Fitting a parallel slopes linear regression
100 xp
Interpreting parallel slopes coefficients
100 xp
Visualizing each explanatory variable
100 xp
Visualizing parallel slopes
100 xp
Predicting parallel slopes
50 xp
Predicting with a parallel slopes model
100 xp
Visualizing parallel slopes model predictions
100 xp
Manually calculating predictions
100 xp
Assessing model performance
50 xp
Comparing coefficients of determination
100 xp
Comparing residual standard error
100 xp
2. 2

Interactions

Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

3. 3

Multiple Linear Regression

See how modeling and linear regression make it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

4. 4

Multiple Logistic Regression

Extend your logistic regression skills to multiple explanatory variables. You’ll also learn about logistic distribution, which underpins this form of regression, before implementing your own logistic regression algorithm.

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Sets De Datos

Ad conversionCustomer churnTaiwan real estateFish measurement dataeBay auctions

Maarten Van den Broeck

Senior Content Developer at DataCamp

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