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Abstract: To summarize, the simplest form of Linear Regression is a type of “curve fitting” to fit a straight line to a trend that lives in data. The data is generally represented by an X variable and a Y variable and is frequently plotted on an Excel “Scatter Chart” as a “Linear Trendline”. Despite its simplicity, it’s a powerful tool that can be used to predict, infer, and forecast many different trends based on historical data. This also fits into the world of DBAs for forecasting when disks will run out of room or how much disk to purchase to “last another year”. More than just a cleverly drawn line on a scatter chart, the “Linear Equation” is the key to all such exploitations of such data. On a more advanced note, Linear Regression is considered to be a “foundational concept of machine learning and AI”. In this session, you’ll be introduced to what Linear Regression is, how it works, and how to calculate the “Linear Equation” using 2 different methods in SQL in a very high-performance manner, and then how to forecast the future based on the derived “Linear Equation” and related values. If you’ve even been to one of Jeff’s presentations before, you’ll also know that there will be a ton of other useful ancillary information included and that donning a water-cooled helmet is strongly recommended.

Agenda: 6:00-6:30 - Networking and speaker setup time 6:30-6:35 - Opening remarks & Introductions 6:35 - Speaker time!

A supply chain expert shows that happy, motivated workers can make big profits for big box companies - that the service and retail industries can provide well-paying jobs, compete on costs, and win. We speak with Zeynep Ton, Professor of Operations Management at MIT Sloan, author of "The Good Jobs Strategy" and founder of the Good Jobs Institute, about how good jobs can be great for business, and what it means for the future of work.