Statistics Simplified: A Step - By - Step Guide

Learn Statistics From Basic to Advance Techniques

What you'll learn

Introduction to Statistics

What is the type of Statistics

Importance of Statistics

Measure of Central Tendency

Advantages and Disadvantages of Mean ,Median, and Mode

Conversion of Ungrouped data to Grouped data

Measure of Dispersion

Data visualization

Basic concepts of Probability

Properties of Probability

Introduction to Conditional Probability

Introduction to Bayes Theorem

Introduction to Random Variable

Introduction to Mathematical Expectation

Introduction to Distributions

Introduction to Sampling Distribution

Introduction to Central Limit Theorem

Introduction to Estimation and Confidence Interval

Hypothesis Testing

Concept of Linear Regression and Correlation Coefficient

Introduction to Experimental Design(ANOVA)

Requirements

Idea of Basic Mathematics

Laptop Computer/Smart Phone with Internet connection

No programming Required

Willingness and zeal to learn new things

Description

Statistics Course DescriptionThis course provides a comprehensive introduction to statistics, covering fundamental concepts and techniques. Students will learn to collect, analyze, and interpret data to make informed decisions. What are Statistics? Definition, importance, and application of statistics. Types of Statistics: Descriptive and inferential Statistics. Types of data: qualitative and quantitative data, level of measurement (Nominal, Ordinal, Interval, and Ratio).v Ungrouped vs Grouped data: Differences and applications. Measure of central tendency: Mean, median, Mode. Measure of dispersion: Range, Variance, Standard Deviation, Mean Deviation Introduction to probability: Basic concepts, Rules, and applications. Introduction to Distribution: Normal distribution, Binomial distribution and Poisson distribution. Introduction to sampling distribution: Concepts, importance, and applications.      Random variable, discrete and continuous random variables. Hypothesis testing, Types of error, Types of hypotheses. Experimental Design (ANOVA): Principles, types and applications. Linear Regression and correlation coefficient: Simple linear Regression, correlation coefficient and interpretation.Key Take away : Under Statistical concepts and techniques Collect, analyze, and interpret data. Apply statistical methods to real-world problems. Make informed decision based on data analysis,Course Objectives: Develop statistical literacy and critical thinking skills. Apply statistical techniques to solve problems.v Interpret and communicate statistical results effectively.This course provides a solid foundation in statistics, preparing students for further study, enhanced researchers to understand the concepts of statistics as well as academician, for practical applications in various fields.

Overview

Section 1: Introduction

Lecture 1 Welcome to my Tutorial

Section 2: Introduction to Statistics

Lecture 2 Introduction to Statistics

Lecture 3 Types of Data

Lecture 4 Level of Measurement

Lecture 5 Descriptive Statistics

Lecture 6 Advantages and Disadvantages of Mean

Lecture 7 Grouped and Ungrouped Data

Lecture 8 Mean of Ungrouped Data

Lecture 9 Conversion of Ungrouped Data to Grouped Data

Lecture 10 Advantages and Disadvantages of Median

Lecture 11 Median of an Ungrouped Data

Lecture 12 Median of Grouped Data

Lecture 13 Advantages and Disadvantages of Mode

Lecture 14 Modal Example for an Ungrouped Data

Lecture 15 Modal Example for a Grouped Data

Lecture 16 Measure of Dispersion

Lecture 17 Introduction to Mean Deviation

Lecture 18 Calculating Measure of Dispersion for an Ungrouped Data

Lecture 19 Calculating Measure of Dispersion for a Grouped Data

Lecture 20 Data Visualization

Section 3: Introduction to Basic Probability

Lecture 21 The Basic Concepts of Probability

Lecture 22 Properties of a Probability

Lecture 23 Probability General Formula and other concepts

Lecture 24 Probability Example 1

Lecture 25 Probability Example 2

Lecture 26 Introduction to Conditional Probability

Lecture 27 Example of Conditional Probability

Lecture 28 Introduction to Bayes Theorem

Lecture 29 Example of Bayes Theorem

Section 4: Introduction to Random Variable

Lecture 30 What is Random Variable

Lecture 31 Summary of Random Variable

Lecture 32 Probability Mass Function

Lecture 33 Probability Density Function

Lecture 34 Introduction to Mathematical Expectation

Lecture 35 Mathematical Expectation for Discrete Random Variable

Lecture 36 Mathematical Expectation for Continuous Random Variable

Section 5: Introduction to Distributions

Lecture 37 What is distribution

Lecture 38 Introduction to Binomial distribution

Lecture 39 Examples of Binomial distribution

Lecture 40 Introduction to Poisson distribution

Lecture 41 Example of Poisson distribution

Lecture 42 Poisson approximation to Binomial distribution

Lecture 43 Introduction to Standard Normal distribution

Lecture 44 Properties of a Standard Normal Distribution

Lecture 45 Normal distribution Example 1

Lecture 46 Normal distribution Example 2

Lecture 47 Normal distribution Example 3

Section 6: Introduction to Sampling distribution

Lecture 48 What is Sampling distribution 1

Lecture 49 What is Sampling distribution 2

Lecture 50 Sampling with replacement example

Lecture 51 Sampling without replacement example

Section 7: Introduction to Central Limit Theorem

Lecture 52 Central Limit Theorem for Mean with example

Lecture 53 Central Limit Theorem for Population Proportion

Lecture 54 Example of Central Limit Theorem for Population Proportion

Section 8: Introduction to Estimation and Confidence Intervals

Lecture 55 What is Statistic and Parameter

Lecture 56 Properties of a Good Estimator

Lecture 57 Introduction to a Confidence Intervals

Lecture 58 Uses of a Confidence Intervals

Lecture 59 Example of a Confidence interval

Lecture 60 Calculation of Sample Size for mean

Lecture 61 Calculation of Sample Size for Proportion

Section 9: Introduction to Hypothesis Testing

Lecture 62 What is Hypothesis Testing

Lecture 63 Simple and Composite Hypothesis Testing

Lecture 64 P-value and Test Statistic function

Lecture 65 Type 1 and Type 2 Error

Lecture 66 Hypothesis Testing Example 1

Lecture 67 Hypothesis Testing Example 2

Lecture 68 Hypothesis Testing Example 3

Lecture 69 Hypothesis Testing Example 4

Lecture 70 Hypothesis Testing Example 5

Section 10: Introduction to Linear Regression Analysis and Correlation Cofficient

Lecture 71 Simple Linear Regression Analysis

Lecture 72 Assumptions of Linear Regression Analysis

Lecture 73 Linear Regression Model

Lecture 74 Linear Regression equation Model generated

Lecture 75 The Linear Regression Analysis Example

Lecture 76 Introduction to Correlation Coefficient

Lecture 77 The Scatterplot and its Meaning

Lecture 78 Correlation Coefficient and Coefficient of Determination

Section 11: Introduction to Experimental Design

Lecture 79 What is Experimental Design

Lecture 80 Experimental Design Terminologies

Lecture 81 Assumptions of Experimental Design(ANOVA)

Lecture 82 Completely Randomized Design(CRD)

Lecture 83 Randomized Complete Block Design(RCBD)

Lecture 84 Latin Square Design (LSD)

Students looking to improve their statistical knowledge for exams or projects,Professional in fields like business,healthcare ,or social sciences who need data analysis skills,Professors in without Statistical background,Anyone curious about learning statistics from basic to advance level,Anyone interest in understanding and applying statistical concepts to solve world data problem