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.

  11. Week 10: Interactions — We extend multiple regression to allow the effect of one predictor (X) to depend on the level of another predictor (Z). In additive models, the X→Y slope is the same at all levels of Z; in interaction models, we allow the X→Y slope to change with Z.

  12. Week 11: Nonlinear Effect, Nonlinear Interactions, and a Peak at 3 Way Interactions — This week we start considering what happens when we let the effect X on Y vary not only over the course of X - in other words, to curve - but also for that curve to depend on Z. Then we really push things by starting to consider 3 way interactions.

  13. Week 12: One Way ANOVA — This week we start to consider categorical predictors and the complexity (but also much less significant than the name change from “regression” to “ANOVA” might suggest…spoiler, it’s the same model!) that doing so introduces.

  14. Week 13: Intro to Conjoint Analysis — In this exercise, we take bits and pieces of all we’ve learned across the previous exercises to dip our toes in the conjoint waters. Conjont is one of the only TRUE marketing-specific analyses out there - we created this technique. We’re going to explore an introductory, simple version.

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