About This Chapter
Forecasting Techniques in Business - Chapter Summary
Understanding forecasting techniques in business is made easier with help from this short chapter. Get quality insights from top instructors who closely examine regression analysis, forecasting and other related topics. Access video and text lessons whenever and wherever your schedule permits. When finished, you will have the knowledge to do the following:
- Describe the strategic role, components and types of forecasting
- Explain how to use regression analysis in business
- Define learning curve analysis in business
- Discuss expected values of perfect information in business
Feel free to navigate this chapter in any manner that suits your study habits. Review lessons in the morning, afternoon or nighttime, and visit them once or multiple times. Click the Quiz tab with each lesson to assess your knowledge with a short multiple-choice assessment. If you want a broader review of the lessons, take the more comprehensive practice chapter exam.
1. Forecasting: Strategic Role, Components & Types
Until companies can acquire a crystal ball in order to look to the future, the best they will be able to do in trying to predict their next moves comes from forecasting. In this lesson, we look at how it is used and the different types.
2. Using Regression Analysis in Business
Statistical analyses and models, like regression analysis, can be extremely helpful when businesses need to forecast their financials and product demand. In this lesson, we'll learn how regression analysis can help accomplish these goals.
3. Learning Curve Analysis in Business: Definition & Examples
We have all experienced a learning curve, but it can be applied to business perspective as well. Learn the definition of a learning curve, the formula, and follow some examples.
4. Expected Values of Perfect Information in Business
When making a business decision, the more information you have, the better. But how much is information worth to a company? This lesson will help explain the expected values of perfect information.
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