Inferential Statistics : Master Hypothesis Testing
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# Inferential Statistics : Master Hypothesis Testing

Get a thorough understanding of one of the most important concepts in Statistics - Hypothesis Testing
4.0 (25 ratings)
854 students enrolled
Created by Shubham Kalra
Last updated 2/2017
English
Current price: \$10 Original price: \$50 Discount: 80% off
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Includes:
• 7 hours on-demand video
• 3 Articles
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Get a complete and thorough understanding of some of the most important concepts in Statistics
View Curriculum
Requirements
• Basics of Statistics (Random Variable, Probability Distributions etc.)
• Knowledge of MS Excel (Preferred, not necessary)
Description

This course is designed for students who are struggling with Statistics or who are complete beginners in statistics.

How is this course structured?

• In the first 2 sections, I will cover the concepts that are crucial to understand the basics of hypothesis testing - Normal Distribution, Standard Normal Distribution,  Sampling, Sampling Distribution and Central Limit Theorem. (Before you start hypothesis testing, make sure you are absolutely clear with these concepts)
• After covering these concepts in detail, I will start with the basics of hypothesis testing  in Section 3. I will cover three methods to do hypothesis testing - Critical Value Method, Z-Score Method and p-value method.

My approach is hands on :  Concepts, examples and solved problems addressing all the concepts covered in the lectures.

Note : Only Hypothesis Testing in Case of Single Population Mean is covered

Lectures on t-distribution and practice questions are yet to be added

Who is the target audience?
• Students who are new to statistics
• Students who are struggling with statistics
• Students who want a refresher of important statistics concepts in a simple and detailed manner
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Curriculum For This Course
55 Lectures
06:59:13
+
Introduction
1 Lecture 02:32
Preview 02:32
+
Continuous Probability Distributions
25 Lectures 03:11:39
Preview 03:38

Preview 09:39

Area as a measure of probability
05:40

Normal Distribution
01:24

Characteristics of Normal Distribution
09:17

Standard Normal Distributions
02:28

How to calculate probability using Cumulative Probability table
06:13

2 important rules to remember
02:44

3 types of probability calculation
09:10

How to compute z values when probability value is given
03:25

A different type of table to compute probability
06:46

How to calculate probability for any normal distribution
10:42

Application of Normal Distribution
16:28

Revisiting the application
15:32

Normal Distribution : Some Real Life Examples
01:26

Normal Distribution using Microsoft Excel
07:52

Preview 11:19

Practice Question 2
05:46

Practice Question 3
17:41

Practice Question 4
07:49

Practice Question 5
07:11

Practice Question 6
05:38

Practice Question 7
04:36

Practice Question 8
12:53

Practice Question 9
06:22
+
Sampling and Sampling Distributions
9 Lectures 01:01:15
Preview 03:08

Preview 04:33

Simple Random Sample - Finite Population
09:59

Simple Random Sample - Infinite Population
03:20

How to calculate the point estimators of population parameters?
05:52

Sampling Distribution
08:59

Properties of Sampling Distribution
06:38

Central Limit Theorem Explained
05:58

Why large samples are considered to be better predictors of population parameter
12:48
+
Hypothesis Testing
20 Lectures 02:43:54
Basics of Hypothesis Testing
10:53

Preview 06:09

Burden of proof
00:54

Type of test and Rejection Region
11:33

Types of Errors
02:40

Type 1 Error
05:22

Type 2 Error
02:51

Example 1 - Critical Value Method
15:40

Example - Z Score Method
06:01

Example 2 - Critical Value Method
08:55

Example 3 - Critical Value Method
08:49

Introduction to p-value
06:53

Example 1 : p-value method
09:05

Example 2 : p-value method
07:41

Example 3 : p-value method
12:01

Practice Question 1
11:12

Practice Question 2
05:25

Practice Question 3
14:06

Practice Question 4
08:33

Practice Question 5
09:11