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    Test Of Hypothesis, Simplified Example-Based Approach

    Posted By: ELK1nG
    Test Of Hypothesis, Simplified Example-Based Approach

    Test Of Hypothesis, Simplified Example-Based Approach
    Last updated 2/2015
    MP4 | Video: h264, 854x480 | Audio: AAC, 44.1 KHz
    Language: English | Size: 326.44 MB | Duration: 6h 6m

    Friendly guide to formulate and test statistical hypothesis. Beginner-friendly approach is adopted with many examples.

    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