Marketing Research & Analytics

MKT 378

This course provides hands-on training in research design, data collection, and analysis using R. You’ll learn to translate quantitative findings into actionable marketing insights—and build skills that transfer to any data-driven role.

Interactive Tools

These Shiny applications help you explore statistical concepts visually. Play with them to build intuition before diving into the math.

  1. Bagel Run Predictor — Our first attempts at some basic model comparisons…but make it about bagels…

  2. Central Tendency & CLT Explorer — Visualizing samples, distributions, measures of central tendency, and Z scores

  3. Simple Models and F Stats — This week we are beginning to do some very simple model comparisons and start working with new model comparison statistics

  4. Simple Regression Intuition Builder — We’re officially working with model comparisons of mean only versus simple regression models!

More Coming Soon · Links to Shiny apps will be added as they’re migrated to the new site.

Coding Assignments

These assignments build your R skills progressively. Each is designed to be accessible—you don’t need prior programming experience.

Getting Started

  1. Week 1: File Management Practice — Install R/RStudio, set up your folders, learn relative paths

  2. Week 2: Stout Exercise — Practice loading data, intro to model comparison

  3. Week 3: Coffee Errors Exercise — In this exercise you will calculate A LOT (sorry but it’s important for building understanding long term) of different types of error

  4. Week 3: Stout Festival Exercise — New data from our week 2 client! Now we’ll play with different measures of central tendency and error

  5. Week 4: Examining Spread and Standardization — In this exercise we examine why and how to examine the spread or variability of our data. We also have some fun (maybe?) with Z scores

  6. Week 5: PRE, Critical Values, and F Tests — In this exercise we start really working on some more substantial model comparisons and the PRE, critical value, and F Test conversations that allows follow

  7. Week 6: Power, Sensitivity, SESOI, Effect Size, CIs, and OPTIONAL Intro to Bootstrapping — This week we examine different ways of measuring how sure we are of an effect - that we’ll find it, that it is “big enough” to matter, and how to talk about these concerns with statistics or otherwise

  8. Week 8: Simple Regression; Centering and Interpreting Predictors; and CIs For Linear Model Estimates — This week we move on to simple regression model comparisons and have our first little peek at re-centering variables for interpretability.

  9. Week 9: Multiple Regression; Chapter 7 Companion — This exercise is for you to work through while watching the lecture videos and reading the chapter on multiple regression.

  10. Week 9: Multiple Regression Exercise — This week we move from simple regression with one predictor to multiple regression…with…multiple predictors.Topics include: redundancy among predictors, interpreting partial regression coefficients, composite predictors, and standardized coefficients, as well as model comparions featuring overall, block (multi-df), and single predictor comparisons.

More coming · Additional assignments will be added throughout the semester.

Resources

Setup

Before the first assignment, you’ll need:

  1. R — Download from CRAN
  2. RStudio — Download from Posit

I’ll walk through installation in class, but these links have everything you need.

References

Getting Help

Stuck on an assignment? In order:

  1. Re-read the error message carefully
  2. Google the error message (seriously—this is what professionals do)
  3. Check the course discussion board
  4. Come to office hours