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Course Description

One of the main difficulties in business forecasting is the fact that the outcomes of decisions can be influenced by numerous factors. In some cases, a simple regression model will be sufficient, but that is less common than needing multiple predictor variables to build a useful model. Multiple regression allows us to consider more inputs when making a decision and build a mathematical model that more accurately reflects reality. This enables a greater degree of confidence in managerial decisions. As you consider more variables, the possibility can arise that our system is not ideal in other ways. In this lesson, you will examine how to identify these scenarios and compensate for them when designing a predictive model. The use of these additional tools can result in a decision making process that is more accurate and better supported. For the best result, complete "Data Analysis and Probability: Create Effective Graphs," "Data Analysis and Probability: Calculate the Probability of an Outcome," "Decision Analysis: Create a Decision Tree," "Continuous Distributions: Apply Normal Distribution and the Z Table," "Sampling: Apply Sampling to Data Analysis," "Hypothesis Testing: Analyze Sample Data to Evaluate Claims," and "Simple Regression: Make Predictions Using Simple Regression" prior to taking this lesson.

Benefits to the Learner

  • Improve a predictive model by incorporating multiple variables
  • Use a variety of statistical tools to verify the validity of your model
  • Adjust your model to handle cases that are nonlinear or non-normal
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Type
self-paced (non-instructor led)
Dates
Mar 29, 2024 to Dec 31, 2030
Total Number of Hours
1.0
Course Fee(s)
Regular Price $0.00
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