Statistics: Central Limit Theorem And Hypothesis Testing

Get a thorough understanding of the most important concepts in Statistics - Central Limit Theorem and Hypothesis Testing

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 PathakCourse 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

Overview

Section 1: Continuous Probability Distributions - Uniform and Normal Distribution

Lecture 1 Introduction

Lecture 2 Introduction (Written Notes)

Lecture 3 Uniform Distribution

Lecture 4 Uniform Distribution (Written Notes)

Lecture 5 Area as a measure of probability

Lecture 6 Normal Distribution

Lecture 7 Characteristics of Normal Distribution

Lecture 8 Standard Normal Distributions

Lecture 9 How to calculate probability using Cumulative Probability table

Lecture 10 2 important rules to remember

Lecture 11 3 types of probability calculation

Lecture 12 How to compute z values when probability value is given

Lecture 13 A different type of table to compute probability

Lecture 14 How to calculate probability for any normal distribution

Lecture 15 Application of Normal Distribution

Lecture 16 Revisiting the application

Lecture 17 Normal Distribution : Some Real Life Examples

Lecture 18 Normal Distribution using Microsoft Excel

Lecture 19 Practice Question 1

Lecture 20 Practice Question 2

Lecture 21 Practice Question 3

Lecture 22 Practice Question 4

Lecture 23 Practice Question 5

Lecture 24 Practice Question 6

Lecture 25 Practice Question 7

Lecture 26 Practice Question 8

Lecture 27 Practice Question 9

Lecture 28 Share your experience

Section 2: Sampling and Sampling Distributions

Lecture 29 Introduction to the Section

Lecture 30 The Sampling Problem

Lecture 31 Simple Random Sample - Finite Population

Lecture 32 Simple Random Sample - Infinite Population

Lecture 33 How to calculate the point estimators of population parameters?

Lecture 34 Sampling Distribution

Lecture 35 Properties of Sampling Distribution

Lecture 36 Central Limit Theorem Explained

Lecture 37 Why large samples are considered to be better predictors of population parameter

Lecture 38 Share your experience

Section 3: Hypothesis Testing

Lecture 39 Basics of Hypothesis Testing

Lecture 40 Null and Alternate hypothesis

Lecture 41 Burden of proof

Lecture 42 Type of test and Rejection Region

Lecture 43 Types of Errors

Lecture 44 Type 1 Error

Lecture 45 Type 2 Error

Lecture 46 Example 1 - Critical Value Method

Lecture 47 Example - Z Score Method

Lecture 48 Example 2 - Critical Value Method

Lecture 49 Example 3 - Critical Value Method

Lecture 50 Introduction to p-value

Lecture 51 Example 1 : p-value method

Lecture 52 Example 2 : p-value method

Lecture 53 Example 3 : p-value method

Lecture 54 Practice Question 1

Lecture 55 Practice Question 2

Lecture 56 Practice Question 3

Lecture 57 Practice Question 4

Lecture 58 Practice Question 5

Lecture 59 Share your experience

Section 4: Bonus Section

Lecture 60 Bonus Lecture

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