Inferential Statistics: Sampling & Hypothesis Testing
4.6 (69 ratings)
1,138 students enrolled

# Inferential Statistics: Sampling & Hypothesis Testing

Get a thorough understanding of the most important concepts in Statistics - Central Limit Theorem and Hypothesis Testing
4.6 (69 ratings)
1,138 students enrolled
Created by Shubham Kalra
Last updated 6/2020
English
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Current price: \$69.99 Original price: \$99.99 Discount: 30% off
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This course includes
• 7 hours on-demand video
• 3 articles
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Understand Normal & Standard Normal Distribution and feel much more confident in solving the questions
• Build a good intuitive understanding of Central Limit Theorem - One of the most important concepts in Statistics
• Understand the basics and essence of Hypothesis Testing
• Solve exam style questions in a step by step manner with much more confidence
Requirements
• Basics of Statistics (Random Variable, Probability Distributions etc.)
• Knowledge of MS Excel (Preferred, not necessary)
Description

This course is the key to build an excellent understanding of Inferential Statistics. It has students (from over 100 countries) and here is what some of them have to say:

"Well explained sir, every concept is clear as water and the way you explain is very easy to understand the concept. Worth buying this course" ~ K Roshnishree

"The explanations are quite intuitive and the best part is that the course includes practice problems which helps in building the concepts" ~ Swati Sahu

"The detail level coverage of the basic topics is amazing"  ~ Rehana Shake

"This instructor is doing a fine job explaining the statistics concepts"  ~ Frank Herrera

"Very clear examples, thank you sir!"  ~ Gitartha Pathak

Course Description:

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

How is this course structured?

• Section 1 and 2: These 2 sections 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)

• Section 3: This section caters to the basics of hypothesis testing with three methods - 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

Who this course is for:
• 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
Course content
Expand all 55 lectures 06:59:12
+ Continuous Probability Distributions - Uniform and Normal Distribution
25 lectures 03:11:37
Introduction
03:38
Preview 09:39
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
01:24
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 01:01:10
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 02:43:53
Basics of Hypothesis Testing
10:53
Burden of proof
00:53
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