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- March 2022 (Revised March 2022)
- Module Note
Exploratory Data Analysis
By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
This module note provides an overview of exploratory data analysis for an introduction to data science course. It begins by defining the term "data", and then describes the different types of data that companies work with (structured v. unstructured, categorical v....
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- March 2022 (Revised March 2022)
- Module Note
Linear Regression
By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares, and how to...
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- March 2022 (Revised March 2022)
- Module Note
Prediction & Machine Learning
By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional...
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- March 2022 (Revised March 2022)
- Module Note
Statistical Inference
By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic...
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- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless...
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- November 2021
- Article
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative...
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- August 2021
- Case
Data Science at the Warriors
By: Iavor I. Bojinov and Michael Parzen
An introductory case for a data science course, which provides an overview of the data science pipeline.
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- August 2021
- Case
Orchadio’s First Two Split Experiments
By: Iavor I. Bojinov, Marco Iansiti and David Lane
Orchadio, a direct-to-consumer grocery business, needs to conduct its first two A/B tests—one to evaluate the effectiveness and functioning of its newly redesigned website, and one to market-test four versions of a new banner for the website. To do so, it will rely on...
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Keywords:
Information Management;
Technological Innovation;
Knowledge Use and Leverage;
Resource Allocation;
Marketing;
Measurement and Metrics;
Customization and Personalization;
Information Technology;
Internet and the Web;
Digital Platforms;
Information Technology Industry;
Food and Beverage Industry
- August 2021
- Case
Precision Paint Co.
Describes a marketing director about to launch a new process for demand forecasting. Provides data that allow students to do a multivariable regression analysis. A rewritten version of an earlier case.
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- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use...
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- 2021
- Working Paper
Virtual Watercoolers: A Field Experiment on Virtual Synchronous Interactions and Performance of Organizational Newcomers
By: Iavor Bojinov, Prithwiraj Choudhury and Jacqueline N. Lane
Do virtual, yet informal and synchronous, interactions affect individual performance outcomes of organizational newcomers? We report results from a randomized field experiment conducted at a large global organization that estimates the performance effects of “virtual...
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- 2021
- Working Paper
Population Interference in Panel Experiments
By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in...
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- December 2020
- Supplement
Experiment B Box Search Implemented
By: Iavor I Bojinov and Karim R. Lakhani
- November 2020
- Case
Creating a Virtual Internship at Goldman Sachs
By: Prithwiraj Choudhury, Iavor Bojnov and Emma Salomon
Goldman Sachs runs an annual internship for over 3,000 participants, spread across dozens of the firm's global offices. In 2020, the team brought all its resources to bear to transform the internship program into a fully virtual format in just a few short weeks. The...
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- 2020
- Working Paper
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted...
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- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts.
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- Article
The Importance of Being Causal
By: Iavor I Bojinov, Albert Chen and Min Liu
Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments....
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