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Statistical Process Simulation - A Linear Regression Approach
In Statistical Process Simulation - A Linear Regression Approach, you'll learn ...
- The purpose of process simulation
- The difference between the theoretical method and the statistical method and when to use one method or the other
- The methodology for designing a real experiment using the statistical method
- What is the Significant F number and why is it important
- How to use a Relative Weights procedure for optimization
Overview
Preview a portion of this course before purchasing it.
Credit: 3 PDH
Length: 30 pages
Simulation processes are techniques which are used to estimate, predict and forecast behaviors. These tools and techniques can be extremely helpful and can be used as tools to design, reduce cost and time, improve success and / or reduce risks.
This course is designed to illustrate the use of different techniques that can be used to simulate processes. Some processes are well known and there are many accurate mathematical equations that can help to predict the performance of a process with a fairly high degree of accuracy. We will call these Theoretical Methods.
Unlike the theoretical methods, other processes are not as clear or don’t have mathematical equations that can help predict the behavior of some variables in the process. In these cases we have to use experience or experimentation to develop such equations so we can simulate their behavior. This second method will be called Statistical Method.
This course will concentrate only on the Statistical Method.
Learning Objectives
Upon completion of this course, participants will be able to:
- Explain the purpose of process simulation.
- Compare different methods of simulating processes.
- Describe the basics of statistical process simulation and the procedure for solving complex problems.
- Apply linear regression techniques to single-variable and multiple-variable cases.
- Use Microsoft Excel statistical tools for process simulation.
- Develop linear regression formulas and analyze the resulting data.
- Distinguish between correlation and causation among variables.
- Explain interactions among resources and how they affect process outputs.
- Optimize simulation results using Microsoft Excel tools.
Certificate of Completion
You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 15 questions. PDH credits are not awarded until the course is completed and quiz is passed.
| This course is applicable to professional engineers in: | ||
| Alabama (P.E.) | Alaska (P.E.) | Arkansas (P.E.) |
| Delaware (P.E.) | District of Columbia (P.E.) | Florida (P.E. Area of Practice) |
| Georgia (P.E.) | Idaho (P.E.) | Illinois (P.E.) |
| Illinois (S.E.) | Indiana (P.E.) | Iowa (P.E.) |
| Kansas (P.E.) | Kentucky (P.E.) | Louisiana (P.E.) |
| Maine (P.E.) | Maryland (P.E.) | Michigan (P.E.) |
| Minnesota (P.E.) | Mississippi (P.E.) | Missouri (P.E.) |
| Montana (P.E.) | Nebraska (P.E.) | Nevada (P.E.) |
| New Hampshire (P.E.) | New Jersey (P.E.) | New Mexico (P.E.) |
| New York (P.E.) | North Carolina (P.E.) | North Dakota (P.E.) |
| Ohio (P.E. Self-Paced) | Oklahoma (P.E.) | Oregon (P.E.) |
| Pennsylvania (P.E.) | South Carolina (P.E.) | South Dakota (P.E.) |
| Tennessee (P.E.) | Texas (P.E.) | Utah (P.E.) |
| Vermont (P.E.) | Virginia (P.E.) | West Virginia (P.E.) |
| Wisconsin (P.E.) | Wyoming (P.E.) | |
Preview a portion of this course before purchasing it.
Credit: 3 PDH
Length: 30 pages
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