Advanced Regression
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About this Course

Welcome to Advanced Regression
The Problem with Ordinary Regression
Bayesian Generalized Linear Models
What's Different Now
Who This Course is For
Outline and Prequisites
Course Presurvey
Lesson Feedback
Meet your Instructors
Resource Library

Intuitive Bayes Discourse Community
Github Repository and Code Access
Environment Installation with Anaconda
Optional Orientation: Discourse
Optional Orientation: Github
Optional Orientation: Podia
Regression Refresher

Introduction
Exploratory Data Analysis
Making a Plan
The World's Simplest Model
Adding a Slope
Transformations
Accounting for Species
When We Catch New Fish
From Predictions to Insights
Bayesian Workflow and Growing Pains
Lesson Recap
Lesson Feedback
Lesson References
Lesson Exercises
Bambi Deep Dive

Introduction
The World's Simplest Model, Now Simpler
Peeking Under The Hood
Sloping Up
Transformations in Bambi
Modeling Categories
Parameter Identifiability
Understanding Encodings
The Full Model
Predictions
An End to End Trip with Bambi
Lesson Recap
Lesson Feedback
Lesson Exercises
Lesson References
Advanced AB Testing and Link Functions

Introduction
Basic AB Test
AB Testing with Continuous Covariates
Link Functions and Generalized Linear Models
AB Testing End to End
United States Election Forecasting
Lesson Recap
Lesson Feedback Form
Lesson Exercises
Lesson References
Categorical Regression

Introduction
Nerdy crêpes
Our First Categorical model
Killing Me Softmax
Running our Categorical regression
Adding Regressors
Fun with Shapes
Unpleasant Probabilities
Sampling & Diagnosing
Debugging our model
Categorical model with Bambi
Introducing pm.ZeroSumNormal
SALK EDA
Coding the Salk model in PyMC
Sampling the Salk model
Estonia, Bambi styyyyyle!
Lesson Recap
Lesson Exercises
Lesson References
Lesson Feedback
Multinominal Regression

Introduction
Aggregating the Estonian data
Introducing geographical variables
Writing the Multinomial model
Coding the Model
Sampling & Posterior analysis
Taking all effects into account
Posterior Schematics
Taking sample size into account
Defining & Sampling the Bambi Multinomial model
Analyzing the posterior effects
Computing the latent probabilities
Comparing PyMC and Bambi estimates
Posterior Retrodictive Sampling
Lesson Recap
Lesson Exercises
Lesson References
Lesson Feedback
Counting Things

Introduction
Counting Things
Counting Regression
Poisson Distributions in Sports
Soccer Statistics
Home Field Advantage
Zero Inflated Poisson
Zero Inflated Fish
Lesson Recap
Lesson Exercises
Lesson Feedback
Lesson References
Overdispersed Regression

12 segundos de oscuridad
Unanticipated consequences
What's the deal with overdispersion
When the whole is more than the sum of the parts
Negative binomial regression
Overdispersion for all
Beta-binomial to the rescue
The beta-binomial model in action
Counting grasshoppers
Beta-binomial for grasshoppers
Lesson Recap
Lesson Feedback
Lesson References
Lesson Exercises
Hierarchical Models

Introduction
Pulling and Unpooling Extremities
Simple Hierarchical Model
Regularization to the mean
Complete Hierarchical Model
Non-centered hierarchical model
Sampling & Posterior Analysis
In-sample Predictions
Analysis of Variance
LOO comparison
Predicting on new groups
Estonian Hierarchy
Writing down the Hierarchical Model
Coding the Full Model
Hierarchical Multinomial, Deluxe Version
Sampling, Posterior & Post-stratification
Post-stratification in practice
Making post-stratified predictions
Visualizing Strata Predictions
A Deeper Cut
Lesson Recap
Lesson Feedback
Lesson References
Lesson Exercises
Final Notes

Congratulations

Introduction

In this section we'll set the scene for this lesson, explaining how you're

Data scientist at a company that sells fish
Want to create a model to predict the weight of future fish
going to tackle all the challenges in this lesson