Regression with R Course

Regression with R

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

Brochure:

Information may change without notice.

Audience

Data scientists
Programmers
Business analysts
Engineers
Scientists

Pre-Requisites

Leading Training's Introduction to R Programming 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

There are currently no scheduled dates.

Please note that this course needs a minimum of 6 delegates to schedule a course. You can choose to be added to the waiting list by clicking the button below, and we will contact you when we have enough delegates interested. Should we not get enough delegates, we will refund or credit your paid booking.

Add me to the waiting list

Should you need this course urgently, the following options are available:

Pay for 6 delegates (whether you have them or not) and we will schedule the course as soon as possible.
If you have fewer delegates and cannot pay for 6, we can negotiate a shortened course where some of the time will be spent in blended learning - watching videos and doing tutorials and exercises with some contact time with the trainer. We would want to discuss what your core needs are so that we cover those aspects. You need to have paid for 3 delegates at least.

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