## Instrumental variable analysis with a binary outcome

Here is an additional post on instrumental variable (IV) analysis. This follows an exercise where I employed two methods of IV analysis, comparing a Bayesian...

Here is an additional post on instrumental variable (IV) analysis. This follows an exercise where I employed two methods of IV analysis, comparing a Bayesian...

Instrumental variable (IV) analysis is one method for causal inference. This approach relies on using an instrumental variable $Z$ to find the true relations...

I’ve done time-series data with time-to-event models and would like to explore modeling with mixed effects models. I’ll take an interative approach, in the s...

Notes for Chapter 5 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha. I’m using this series of posts to take some notes.

Notes for Chapter 4 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha. I’m using this series of posts to take some notes.

Notes for Chapter 3 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha. I’m using this series of posts to take some notes.

Notes for Chapter 2 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha.

I’m basically a fan-boy of Richard McElreath’s Statistical Rethinking. That’s no secret. But I thought it would be prudent to learn more about causal inferen...

A few years ago, not long after I started writing on this blog, I wrote a piece called The probability of making your Friday night party. Well, the opportuni...

For a while, I’ve wondered about the different approches for multilevel modeling, also known as mixed effects modeling. My initial understanding is with a Ba...

While continuing to deep dive on covariance priors following my prior post, I investigated implementations in pymc. I played around with the LKJcorr and LKJc...

Covariance priors for multivariate normal models are an important tool for the implementation of varying effects. By representing more than one parameter wit...

Multi-level models are great for improving our estimates. However, the intuitive way these kinds of models are specified (which goes by the unhelpful name “c...

One of the lessons from Statistical Rethinking that really hit home for me was the importance of considering the data generation process. Different datasets ...

The value of simulations is highighted by Dr. McElreath throughout his textbook and by van de Schoot and colleagues. I didn’t entirely appreciate its value u...

In my last post, I gave an example of a multilevel model using a binomial generalized linear model (GLM). The varying intercept model helped illustrate pa...

I’ve been on a journey learning multilevel models and Bayesian inference through Richard McElreath’s Statistical Rethinking book. The concepts of shrinkage a...

Recently, I built a simple NLP algorithm for a work project, following the template described in this tutorial. As I looked to increase my model’s complexity...

At last, we have come to the end. This is the final post in a series of linear regression posts using PyMC3, from my reading of Statistical Rethinking. Part ...

This is the next post in a series of linear regression posts using PyMC3. This series has been inspired by my reading of Statistical Rethinking. Part 1 was d...

In a previous post, I wrote about my inital experience using PyMC3. The point was to take a dive deep into some of the package’s objects using a linear regre...

I previously wrote about my discovery of Statistical Rethinking. The book’s title could not be more spot-on–it’s helped me look at statistics in a different ...

In the last post, we learned about the beta distribution and why it would be a more realistic prior in the context of our problem. We also selected appropria...

I meant to post this some time ago, but I have been busy. But with the baseball example I am using, it is only fitting that I post this now, just after this ...

In my previous post, we saw how Bayes’ theorem was applied to a relatively simple problem with Bertrand’s box paradox. Here I’ll talk about another applicati...

Bayes’ theorem is one of the most useful applications in statistics. But sometimes it is not always easy to recognize when and how to apply it. I was doing s...

A few weeks ago, while making a histogram in a SQL query, I discovered that some solutions out there do not include bins with 0 counts. This bugged me so I f...

I recently read this passage in the section on multiple linear regression from the fantastic book Introduction to Statistical Learning:

Problem statement

PostgreSQL is one of the most popular variants of SQL. It is common to use PostgreSQL with pgadmin but I am not a big fan of their UI. By contrast, interacti...

One of the things about Python that I haven’t fully appreciated are the use of iterators. I’ll go over some iterators that are a part of base Python and then...

Seems like every statistics class starts off with a coin toss. It’s simple enough for me. Some fancy teachers might start right off the bat and get into the ...

My wife and I have enjoyed living in the Bay Area where we’ve been able to satisfy our love of outdoor activities while being near a cool city. While we’re f...

I recently finished Andrew Ng’s fantastic and well-known Machine Learning course through Coursera. As I progress into my data science journey, I felt that ta...

I coded for a couple of years in R but switched over to Python almost a year ago. I have to say that I miss R’s ggplot2. Like a lot.

Here is an additional post on instrumental variable (IV) analysis. This follows an exercise where I employed two methods of IV analysis, comparing a Bayesian...

Instrumental variable (IV) analysis is one method for causal inference. This approach relies on using an instrumental variable $Z$ to find the true relations...

I’ve done time-series data with time-to-event models and would like to explore modeling with mixed effects models. I’ll take an interative approach, in the s...

Notes for Chapter 5 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha. I’m using this series of posts to take some notes.

Notes for Chapter 4 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha. I’m using this series of posts to take some notes.

Notes for Chapter 3 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha. I’m using this series of posts to take some notes.

Notes for Chapter 2 of Causal Inference with Survey Data on LinkedIn Learning, given by Franz Buscha.

I’m basically a fan-boy of Richard McElreath’s Statistical Rethinking. That’s no secret. But I thought it would be prudent to learn more about causal inferen...

A few years ago, not long after I started writing on this blog, I wrote a piece called The probability of making your Friday night party. Well, the opportuni...

For a while, I’ve wondered about the different approches for multilevel modeling, also known as mixed effects modeling. My initial understanding is with a Ba...

While continuing to deep dive on covariance priors following my prior post, I investigated implementations in pymc. I played around with the LKJcorr and LKJc...

Covariance priors for multivariate normal models are an important tool for the implementation of varying effects. By representing more than one parameter wit...

Multi-level models are great for improving our estimates. However, the intuitive way these kinds of models are specified (which goes by the unhelpful name “c...

One of the lessons from Statistical Rethinking that really hit home for me was the importance of considering the data generation process. Different datasets ...

The value of simulations is highighted by Dr. McElreath throughout his textbook and by van de Schoot and colleagues. I didn’t entirely appreciate its value u...

In my last post, I gave an example of a multilevel model using a binomial generalized linear model (GLM). The varying intercept model helped illustrate pa...

I’ve been on a journey learning multilevel models and Bayesian inference through Richard McElreath’s Statistical Rethinking book. The concepts of shrinkage a...

Recently, I built a simple NLP algorithm for a work project, following the template described in this tutorial. As I looked to increase my model’s complexity...

At last, we have come to the end. This is the final post in a series of linear regression posts using PyMC3, from my reading of Statistical Rethinking. Part ...

This is the next post in a series of linear regression posts using PyMC3. This series has been inspired by my reading of Statistical Rethinking. Part 1 was d...

In a previous post, I wrote about my inital experience using PyMC3. The point was to take a dive deep into some of the package’s objects using a linear regre...

I previously wrote about my discovery of Statistical Rethinking. The book’s title could not be more spot-on–it’s helped me look at statistics in a different ...

In the last post, we learned about the beta distribution and why it would be a more realistic prior in the context of our problem. We also selected appropria...

I meant to post this some time ago, but I have been busy. But with the baseball example I am using, it is only fitting that I post this now, just after this ...

In my previous post, we saw how Bayes’ theorem was applied to a relatively simple problem with Bertrand’s box paradox. Here I’ll talk about another applicati...

Bayes’ theorem is one of the most useful applications in statistics. But sometimes it is not always easy to recognize when and how to apply it. I was doing s...

I recently read this passage in the section on multiple linear regression from the fantastic book Introduction to Statistical Learning:

Seems like every statistics class starts off with a coin toss. It’s simple enough for me. Some fancy teachers might start right off the bat and get into the ...

My wife and I have enjoyed living in the Bay Area where we’ve been able to satisfy our love of outdoor activities while being near a cool city. While we’re f...

A few weeks ago, while making a histogram in a SQL query, I discovered that some solutions out there do not include bins with 0 counts. This bugged me so I f...

Problem statement

PostgreSQL is one of the most popular variants of SQL. It is common to use PostgreSQL with pgadmin but I am not a big fan of their UI. By contrast, interacti...

Introduction I have identified writing well as a skill I will prioritize. This improved skill will benefit both smaller forms I have taken for granted (like...

This is a summary of a discussion I led through a discussion group. This group is called the “STEM Education & Diversity Discussion Group” and it is orga...

`np.dot`

and broadcasting
Vectorization and broadcasting are tricks I have used sparingly and absent-mindedly if at all. However, it is a critical skill for algorithmic code to run ef...

One of the things about Python that I haven’t fully appreciated are the use of iterators. I’ll go over some iterators that are a part of base Python and then...

This is a summary of a discussion I led through a discussion group. This group is called the “STEM Education & Diversity Discussion Group” and it is orga...

`np.dot`

and broadcasting
Vectorization and broadcasting are tricks I have used sparingly and absent-mindedly if at all. However, it is a critical skill for algorithmic code to run ef...