Test Of Hypothesis, Simplified Example-Based Approach

What you'll learn
How to formulate a hypothesis
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
Requirements
Curiosity and passion to learn should be sufficient
Pre-knowledge in elementary probabilities, statistics, and the normal distribution will be helpful
Description
In this course, students will be introduced to main aspects of statistical hypotheses. A simple approach is adopted to be beginners-friendly with several practical of examples to explain ideas. The course explains and develop skills toward proper hypotheses formulation and testing methodologies. Main points about hypotheses on the mean and variance of a population are covered.Students will learn how to correctly formulate a statistical hypothesis. They will also learn, in step by step, how to perform the right test for various types of hypotheses around the mean or the variance of a population. Interestingly, we will learn different methods to carry out correct hypothesis testing such as: test statistic, using confidence intervals and P-value.The course content is simplified to fit for beginners with minimal background in statistics. Also, it is highly focus on examples and less on complex theories. Certainly, a focus on practice with real world problems will also articulate the feel sense of the value of this knowledge.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. Both software are introduced from absolute zero level, then students are guided with examples on how to use them for general purpose and specifically for working out tests of hypotheses.
Overview
Section 1: Introduction to Hypotheses Testing
Lecture 1 Refreshing about probabilities
Lecture 2 What is a hypothesis?
Lecture 3 Formulation of hypotheses
Lecture 4 Sampling distribution
Lecture 5 Quiz set 1
Section 2: Tests on Population Mean, Population Variance is Known
Lecture 6 Performing the test on the mean
Lecture 7 Confidence intervals
Lecture 8 P-Value
Lecture 9 Quick summary
Lecture 10 One sided tests
Lecture 11 More on one sided tests
Lecture 12 Summary
Lecture 13 Quiz set 2
Section 3: Tests on Population Mean, Population Variance is Unknown
Lecture 14 Performing the test
Lecture 15 Using confidence interval and P-Value
Lecture 16 Example: large sample size
Lecture 17 Quiz set 3
Section 4: Errors in Hypotheses Testing
Lecture 18 Type I and type II errors
Lecture 19 Choosing the sample size
Lecture 20 Quiz set 4
Section 5: Tests on Population Variance
Lecture 21 Performing the test
Lecture 22 One sided tests on the variance
Lecture 23 Confidence interval and P-Value
Lecture 24 Quiz set 5
Section 6: Minitab Software Tutorial
Lecture 25 Introduction to Minitab
Lecture 26 Performing the Z-tet
Lecture 27 Performing the t-test
Lecture 28 Tests on variance
Lecture 29 Quiz set 6
Section 7: Using Matlab for Hypothesis Testing
Lecture 30 Introduction to Matlab 1
Lecture 31 Introduction to Matlab 2
Lecture 32 Using M-Files in Matlab
Lecture 33 One sample Z test using Matlab
Lecture 34 Confidence Interval in Matlab for Z test
Lecture 35 P-Value in Matlab for Z test
Lecture 36 Matlab code files
Lecture 37 Performing t and chi^2 tests in Matlab
Lecture 38 Example: One sided test on variance using Matlab
Section 8: Appendices
Lecture 39 Appendix A: course material in PDF
Lecture 40 Main probability distributions tables (Ref: Montgomery and Runger)
Lecture 41 Operating Characteristic Curves (OCC) (Ref: Montgomery and Runger)
Students interested in statistics and statistical testing,Engineers doing experiments related to statistical measures,People working on data analysis and data interpretations


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