Test of Hypotheses, Simplified Engineering Approach
4.6 (11 ratings)
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Test of Hypotheses, Simplified Engineering Approach

Friendly guide to formulate and test statistical hypotheses. Engineering approach is adopted with lots of examples.
4.6 (11 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,758 students enrolled
Created by Misbah Aiad
Last updated 2/2015
English
Price: Free
Includes:
  • 4 hours on-demand video
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Proper hypotheses formulation
  • Understanding the test statistics
  • Utilizing confidence intervals and P-Values
  • Understanding of concerns about type I and type II errors
  • Make usage of Minitab software to check hypotheses
  • Use Matlab coding to check for hypotheses
View Curriculum
Requirements
  • Pre-knowledge in elementary probabilities and/or statistics
  • Understanding probability distributions, specially normal distribution
Description

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.

Who is the target audience?
  • Students with concerns to statistics field
  • Engineers doing experiments related to statistical measures
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Curriculum For This Course
Expand All 41 Lectures Collapse All 41 Lectures 06:06:26
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Introduction to Hypotheses Testing
5 Lectures 32:43

You will get refreshing information about probabilities, statistics distribution. The focus is given to understand normal probability distribution.

Refreshing, What should you know before?
08:38

What is a hypothesis?
08:01

Formulation of hypotheses
09:13

Sampling distribution
06:51

Quiz set 1
1 page
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Tests on Population Mean, Population Variance is Known
8 Lectures 01:01:34
Performing the test on the mean
12:58

Confidence intervals
07:27

P-Value
09:31

Quick summary
04:11

One sided tests
13:24

More on one sided tests
08:29

Summary
05:34

Quiz set 2
1 page
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Tests on Population Mean, Population Variance is Unknown
4 Lectures 26:16
Performing the test
14:34

Using confidence interval and P-Value
06:02

Example: large sample size
05:40

Quiz set 3
1 page
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Errors in Hypotheses Testing
3 Lectures 16:11
Type I and type II errors
07:11

Choosing the sample size
09:00

Quiz set 4
1 page
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Tests on Population Variance
4 Lectures 35:49

Learn how to use Chi-2 test on a population variance.

Performing the test
15:37

One sided tests on the variance
08:44

Confidence interval and P-Value
11:28

Quiz set 5
1 page
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Minitab Software Tutorial
5 Lectures 19:55

Introduce to Minitab software environment. Basic issues are covered.

Introduction to Minitab
05:42

Performing the Z-tet
05:00

Performing the t-test
05:20

Tests on variance
03:53

Quiz set 6
1 page
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Using Matlab for Hypothesis Testing
9 Lectures 36:58
Introduction to Matlab 1
03:25

Introduction to Matlab 2
06:22

Using M-Files in Matlab
07:08

One sample Z test using Matlab
11:20

Confidence Interval in Matlab for Z test
04:19

P-Value in Matlab for Z test
04:24

Matlab code files
2 pages

Performing t and chi^2 tests in Matlab
1 page

Example: One sided test on variance using Matlab
3 pages
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Appendices
3 Lectures 00:00

This is the full course material in a single pdf file, total of 114 slides.

Appendix A: course material in PDF
114 pages

Main probability distributions tables (Ref: Montgomery and Runger)
4 pages

Operating Characteristic Curves (OCC) (Ref: Montgomery and Runger)
7 pages
About the Instructor
Misbah Aiad
4.6 Average rating
11 Reviews
1,758 Students
1 Course
Engineer, Tutor

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.