Python Regression Course

Python Regression

Details

Performing regressions is a powerful method to help you understand the relationships between your variables, which makes it a must for anyone working with data. Thankfully, we can use the Python language to make this important task much easier and more efficient.

In this course you will learn to:

Perform regressions using Python
Analyse the strength of correlations
Set conditional expectations
Analyse the regression line
Employ linear models in real-life scenarios
Understand counfounding
Perform Lease Squares Estimates
Understand association vs causation

Delivery Methods

Leading Training is focusing on providing virtual training courses for the foreseeable future and will only consider in-person and classroom training on request, with a required minimum group size of six delegates. We remain committed to offering training that is fast, focused and effective.

Delivery Method	Duration	Price (excl. VAT)
Classroom	4 Days	ZAR 11,000.00	Get a Quote
Live Virtual Training	4 Days	ZAR 9,000.00	Get a Quote

Discounts Available

Save up to 10% by booking and paying 10 business days before the course.

Brochure:

Information may change without notice.

Audience

Data scientists
Programmers
Business analysts
Engineers
Scientists

Pre-Requisites

Leading Training's Python Course or equivalent knowledge

Course Outline / Curriculum

Regression

Case study: is height hereditary?
The correlation coefficient
- Sample correlation is a random variable
- Correlation is not always a useful summary
Conditional expectations
The regression line
- Regression improves precision
- Bivariate normal distribution (advanced)
- Variance explained
- Warning: there are two regression lines
Exercises

Linear models

Case study: Moneyball
- Sabermetics
- Baseball basics
- No awards for BB
- Base on balls or stolen bases?
- Regression applied to baseball statistics
Confounding
- Understanding confounding through stratification
- Multivariate regression
Least squares estimates
- Interpreting linear models
- Least Squares Estimates (LSE)
- The lm function
- LSE are random variables
- Predicted values are random variables
Exercises
Linear regression in the tidyverse
- The broom package
Exercises
Case study: Moneyball (continued)
- Adding salary and position information
- Picking nine players
The regression fallacy
Measurement error models
Exercises

Association is not causation

Spurious correlation
Outliers
Reversing cause and effect
Confounders
- Example: UC Berkeley admissions
- Confounding explained graphically
- Average after stratifying
Simpson’s paradox
Exercises

Schedule Dates and Booking

To apply for a booking, click on the relevant "Book Now" button below.
Note: places are only fully secured once payment has been made.

Start Date	End Date	Branch	Status
Mon 7 Apr 2025	Thu 10 Apr 2025	Fourways	Scheduled	Book Live Virtual Training

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