In this video, the instructor demonstrates the meaning of confidence intervals around a forecast.
- [Instructor] Now we've run our regression…and we have coefficients that we can use…to make a prediction,…but what we don't know yet…is how confident we can be in those coefficients.…We don't have a way to stress test those coefficients…to figure out whether or not the predictions…that we're going to make based on them…are going to be particularly accurate or not.…That's what we're going to talk about in just a moment.…I'm in the 0402 folder using the begin financial data file.…
If you recall, we've got a variety of different data points…almost 400,000 rows of data,…and roughly a dozen different variables…covering various corporate financial characteristics…along with sales of the firm,…and we were trying to predict the sales…of a company based on that.…To that end, in sheet one we ran this regression,…and we saw that the regression was reasonable overall.…We had a 45% R squared…and our coefficients looked highly statistically significant…based on the P values.…
But what we don't know yet is whether or not…those coefficients are particularly precise or accurate.…
Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.
- Understanding big data and predictive analytics
- Gathering financial data
- Cleaning up your data
- Calculating key financial metrics
- Using regression analysis for business-specific forecasts
- Performing scenario analysis
- Calculating confidence intervals
- Stress testing