Learn how to perform regression analysis using R and how to interpret the results.
- [Instructor] So let's get started…with our regression analysis for R.…I've already got the application opened,…so R Studio is here on our desktop.…And I'm going to navigate over to our exercise files…and open up zero three zero two.…I'm going to select regression dot R.…And at the outset of any analysis,…we need to connect to our data,…so we've already done that here,…but let's select that line and run it…so we can bring that data connectivity into R Studio…so select that line, click Run.…
Let's go ahead and take a look at our data.…I'm just going to double click on my regression analysis…in the Environment pane.…On the right side of your screen, double click.…And this helps us to see what we have to work with here…in terms of our case study.…So it looks like we have some data related to…date, geography, out of home, print and broadcast,…those seem like marketing channels.…And then we have net sales and it looks like…a few other indicators here.…
For all intents and purposes,…for our particular case study,…what I think it would make sense to do…
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment
Skill Level Intermediate
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1. Software Installation
2. Data, Exploratory Analysis, and Performance Analysis
3. Inference and Regression Analysis
5. Cluster Analysis
6. Conjoint Analysis
7. Best Practices
Next steps1m 8s
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