Data Pipelines

The most challenging aspect of effective data analysis is often getting your data into a usable structure. Learn how Dead Reckoning can provide customized, stable solutions for your use case.

Predictive Analytics

Apply a principled model design approach that maximizes opportunities for informed decision-making given your data, goals, and resources

Customized Dashboards & Reports

Leverage your data processing and modeling pipelines to generate dashboards and reports that deliver actionable insights and communicate key information to stakeholders

What’s in a Name?

Learn more about Dead Reckoning as an approach to navigating data science challenges and opportunities

The Dead Reckoning Philosophy

From the blog

Recent posts

Visualizing Variance in Multilevel Models Using the Riverplot Package

By Matthew Barstead, Ph.D. on January 19, 2019

Spurred on by Alex Shackman, I have been working to figure out a good way to visualize different sources of variation in momentary mood. The most common way of visually depicting variance decompositions from the sort of multilevel models we used to analyze our data is a stacked bar plot. So that seemed like a good place to start. Figure 1. Stacked Barplot of Model Variance Decomposition Now, choosing a color scheme that screams “HI I’M A COLOR!

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A Rose by Any Other Name: Statistics, Machine Learning, and AI (Part 2 of 3)

By Matthew Barstead, Ph.D. on December 3, 2018

In the first post in this series, I described the impetus for this trek through statistical modeling, machine learning and artificial intelligence. I also provided an initial set of comparisons for three different approaches to classification: k-means, k-nearest neighbor, and latent profile analysis (model-based clustering). If you want to check those mini-walkthroughs out click here. As a reminder, my goal here is to compare and contrast different approaches to data analysis and predictive modeling that are, in my mind, arbitrarily lumped into statistical modeling and machine learing/artificial intelligence categories.

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A Rose by Any Other Name: Statistics, Machine Learning, and AI (Part 1 of 3)

By Matthew Barstead, Ph.D. on November 29, 2018

I was recently interviewing for a job and a recruiter asked me if I wanted to enhance aspects of my machine learning background on my resume before she passed it on for the next round of reviews. I resisted the urge to chide her in the moment by pointing out the flawed distinction between statistics and machine learning, an unnecessary admonishment that would have been to no one’s benefit.

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Bayesian Multilevel Model with Missing Data Complete Workflow (Part 2 of 3)

By Matthew Barstead, Ph.D. on September 3, 2018

Overview: This is the second post in a three-part blog series I am putting together. If you have not read the first post in this series, you may want to go back and check it out. In this post, I will focus on running and evaluating the imputation model itself, having identified the appropriate covariates that help account for missingness in the first post. Data Brief Description: The data in question come from a study that involved a one-week ecological momentary assessment (EMA) protocol.

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