I hope you are having a great Labor Day holiday!
The next few weeks we will be covering topics that will help you go from data to insight for your business. This week I’ll talk about some simple statistics that you should have in your analytics toolkit. Next week I’ll dig into some best practices in data visualization that will help you deliver the insights you’ve uncovered. Finally, I’ll spend a week showing you how to do the same analysis using a different tool each day, with the goal to highlight the pros and cons of each tool.
Before we do that though, let me (re)introduce myself. I’m Doug Mitarotonda, the Head of Customer Development at Outlier, and I’ve been working in data analytics (and pricing¹) throughout my career, most recently helped sports teams and live entertainment promoters analyze their ticket pricing strategies.
This week we will talk about a few of the most common descriptive statistics used to understand data and then move on to predictive modeling using a linear regression (a topic Sean mentioned last week) at the end of the week.
- Central tendency: mean, median, and mode
- Dispersion: range, variance, standard deviation
- Relationship: covariance and correlation
- Prediction: linear regression
While going through these topics I’ll refer back to my hypothetical company, Doug’s Desserts (because I love baking!), which sells baked goods in a storefront and online and also has an online subscription service that provides customers with recipes and tips, in order to provide context for examples.
 If you missed my two-week session on pricing a few months ago and would like to know more, please send me an email.
Questions? Send any questions on data analytics or pricing strategy to email@example.com and I’ll answer them in future issues!