I’m pleased to present a new O’Reilly Online Learning session on Advanced Excel Statistics for Business Analytics on Wednesday August 12th at 1pm Eastern.
The session is free to attend for all subscribers to the fantastic O’Reilly Online Learning platform. Check your employer or university for an institutional account, or really, consider signing up — and not just for this session! You’ll get access to an untold number of similarly insightful workshops and a vast multimedia library.
Learning statistics in Excel
I hold that Excel is the best way to learn statistics (Don’t @ me!). As a “visual calculator,” it’s such an engaging tool to internalize the foundations of analytics. That’s why I choose to conduct this series in Excel. The introductory workshop has gone quite well, so here’s the sequel!
Workshop agenda below:
Regression analysis and predictive models (55 minutes)
- Presentation: Multiple linear regression—explaining the relationship between a continuous dependent variable and two or more continuous variables; logistic regression—explaining the relationship between a dichotomous dependent variable and two or more independent variables; interpreting probabilities and evaluating predictive accuracy
- Hands-on exercise: Practice building and analyzing multiple regression models
- Q&A
Break (5 minutes)
Simulation and optimization (55 minutes)
- Presentation: Monte Carlo simulation—modeling the probability of different business outcomes; statistics and optimization—the relationship between statistics and optimization, optimizing one variable with Goal Seek, optimizing multiple variables with Excel Solver
- Hands-on exercise: Build models using simulation and optimization techniques
- Q&A
Break (5 minutes)
Forecasting and time series (60 minutes)
- Presentation: Building a forecast—establishing a baseline, building forecasts with rolling averages and exponential smoothing, evaluating performance
- Hands-on exercise: Forecast a time series
- Q&A
You can see the complete agenda and register at O’Reilly. A recording will also be made available to registrants.
You can also view the preliminary slides, datasets and demo notes at the course’s GitHub repo.
I hope to see you there — the more attendees I get, the more awesome programs I can provide.
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