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.…
Author
Michael McDonald
Released
7/19/2017Join 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.
LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.
The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.
- List the two methods of making decisions.
- Identify the most common method of conventional financial forecasting.
- Describe common challenges that come when trying to merge data.
- Assess the types of questions that business intelligence is best suited to answer.
- Distinguish the statistic that is most useful for estimating the impact of an X variable on a Y variable.
Skill Level Intermediate
Duration
Views
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Introduction
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Welcome1m 11s
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What you should know1m 40s
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Exercise files38s
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1. The Basics
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What is big data?4m 5s
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2. Forecasting in Finance
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Gather financial data6m 48s
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Clean financial data7m 33s
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3. Performing Forecasting
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Regressions for forecasting2m 30s
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Use Excel for regressions4m 26s
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4. Interpreting Forecast Results
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What do the results mean?5m 46s
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Perform stress testing4m 9s
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Conclusion
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Wrap up1m 16s
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Video: Confidence intervals around the result