In this video, the instructor interprets the results of a regression forecast.
- [Instructor] Jack and Diane are back!…Jack and Diane have been following along with us,…learning about financial forecasting as we have.…Now they're ready to put what they've learned into practice.…Jack wants to do a sales forecast to figure out where…his company is going in comparison to their competitors.…While Diane wants to go through an optimized pricing,…to determine its impact on sales.…Now, let's take a look at some data…that can help both of them.…
I'm in the zero four zero one folder,…looking at the begin financial data file.…And as you can see we've got a very different dataset,…than the employee theft data we worked with before.…This dataset has almost 400,000 rows of data.…And roughly a dozen different variables.…This is essentially all of the data that Excel can handle.…If you have any more data than this you'll probably need…to move to a professional software package,…something like SAS or Stata or R.…
I've certainly done projects using those kinds of software.…And your company perhaps has enough data that it might…
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.
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.