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In this course, students will be introduced to main aspects of statistical hypotheses. A simple engineering approach is adopted with several of examples to explain ideas. The course explains and develop skills toward proper hypotheses formulation and testing methodologies. Major points about hypotheses on the mean and variance of a population are covered.
Finally, we make the use of both Minitab and Matlab software to help us quickly perform our tests. Friendly tutorials are given with examples for both software.
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|Section 1: Introduction to Hypotheses Testing|
You will get refreshing information about probabilities, statistics distribution. The focus is given to understand normal probability distribution.
What is a hypothesis?
Formulation of hypotheses
Quiz set 1
|Section 2: Tests on Population Mean, Population Variance is Known|
Performing the test on the mean
One sided tests
More on one sided tests
Quiz set 2
|Section 3: Tests on Population Mean, Population Variance is Unknown|
Performing the test
Using confidence interval and P-Value
Example: large sample size
Quiz set 3
|Section 4: Errors in Hypotheses Testing|
Type I and type II errors
Choosing the sample size
Quiz set 4
|Section 5: Tests on Population Variance|
Learn how to use Chi-2 test on a population variance.
One sided tests on the variance
Confidence interval and P-Value
Quiz set 5
|Section 6: Minitab Software Tutorial|
Introduce to Minitab software environment. Basic issues are covered.
Performing the Z-tet
Performing the t-test
Tests on variance
Quiz set 6
|Section 7: Using Matlab for Hypothesis Testing|
Introduction to Matlab 1
Introduction to Matlab 2
Using M-Files in Matlab
One sample Z test using Matlab
Confidence Interval in Matlab for Z test
P-Value in Matlab for Z test
Matlab code files
Performing t and chi^2 tests in Matlab
Example: One sided test on variance using Matlab
|Section 8: Appendices|
|Lecture 39||114 pages|
This is the full course material in a single pdf file, total of 114 slides.
Main probability distributions tables (Ref: Montgomery and Runger)
Operating Characteristic Curves (OCC) (Ref: Montgomery and Runger)
A PhD candidate in the school of Electrical and Electronic Engineering at Nanyang Technological University in Singapore. My research interests are in areas of energy management, applied statistics, optimization engineering and machine learning.