After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Prerequisites
Curriculum
Course outline
1
Parallel Slopes
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.
- Parallel slopes linear regression50 XP
- Fitting a parallel slopes linear regression100 XP
- Interpreting parallel slopes coefficients100 XP
- Visualizing each explanatory variable100 XP
- Visualizing parallel slopes100 XP
- Predicting parallel slopes50 XP
- Predicting with a parallel slopes model100 XP
- Visualizing parallel slopes model predictions100 XP
- Manually calculating predictions100 XP
- Assessing model performance50 XP
- Comparing coefficients of determination100 XP
- Comparing residual standard error100 XP
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
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
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.
Intermediate Regression with statsmodels in Python
Course
Complete
