# Statistics Made Easy by Example for Analytics data science

Statistics Simplified – Statistics Made Easy by Excel Simulations. Master fundamentals of statistics & Probability.

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## Statistics Made Easy by Example for Analytics data science

### What you’ll learn

By the end of this course, you should become very comfortable with popular concepts of statistics
You should know the genesis of popular statistical concepts
You should know how you apply it in business problem
You should have the required course material for referral

### Requirements

Familiarity with Microsoft Excel basics
Students should be able to check formula used in excel after downloading

### Description

What is the course about?
This course promises that students will
Learn the statistics in a simple and interesting way Know the business scenarios, where it is applied See the demonstration of important concepts (simulations) in MS Excel Practice it in MS Excel to cement the learning Get confidence to answer questions on statistics Be ready to do more advance course like logistic regression etc.
Course Material
The course comprises of primarily video lectures. All Excel file used in the course are available for download. The complete content of the course is available to download in PDF format.
How long the course should take?
It should take approximately 25 hours for good grasp on the subject.
Why take the course
To understand statistics with ease Get crystal clear understanding of applicability Understand the subject with the context See the simulation before learning the theory

### Overview

Section 1: Probability and Expectations

Lecture 1 Welcome Note

Lecture 2 Section Overview

Lecture 3 Relative Frequency and Probability with Excel simulation

Lecture 4 How to download excel files etc.

Lecture 5 Probability Example of rolling one and two dice

Lecture 6 Probability distribution function – descrete and continuous

Lecture 7 Expectation or Expected Value

Lecture 8 Expected Value of a Carnival Game

Lecture 9 Expected value of Casino Game

Lecture 10 Section PDF

Section 2: Central Tendencies and Dispersion

Lecture 11 Section Overview

Lecture 12 Arithmetic Mean

Lecture 13 Advantage n Disadvantage of Arithmatic Mean

Lecture 14 Geometric Mean and Its applicability

Lecture 15 Weighted Mean

Lecture 16 Median and Its Calculations

Lecture 17 Advantage and Applicability of Median

Lecture 18 Mode Its Advantage and Usage

Lecture 19 Dispersion: Why you shd Know

Lecture 20 Range and Its Advantage and Disadvantage

Lecture 21 Average Absolute Difference

Lecture 22 Variance and Standard deviation

Lecture 23 Note: Square Error Is Minimum around Mean

Lecture 24 Coefficient of Variance and Z statistics

Lecture 25 Exercise – caculate central tenedencies, dispersion etc.

Lecture 26 Section PDF

Section 3: Central Limit Theorem

Lecture 27 Section outline

Lecture 28 Frequency Distribution

Lecture 29 Normal Distribution and its properties

Lecture 30 Real Life Example of Normal distribution

Lecture 31 Normal distribution due to aggregation

Lecture 32 CLT concepts and demo

Lecture 33 Validate properties of Normal Distribution

Lecture 34 Section PDF

Section 4: Sampling Distribution

Lecture 35 Section outline

Lecture 36 Terms Associated with Sampling Distribution

Lecture 37 Examples of Sample Statistic

Lecture 38 Sampling distribution of Means

Lecture 39 Sampling Distribution of proportion

Lecture 40 Optional topic – Sampling distribution of means and proportions with IID series

Lecture 41 Point Estimate and Interval Estimate

Lecture 42 Intuitive Understanding and Demo of confidence Interval

Lecture 43 Formal defintions and table for confidence interval

Lecture 44 Calculation example of confidence interval for sample proportions

Lecture 45 Confidence Interval for Mean

Lecture 46 Demo of confidence Interval for Mean

Lecture 47 Example of Confidence Interval Calculation

Lecture 48 Preamble for small sample statistic

Lecture 49 Demo of T Distribution

Lecture 50 Confidence Interval Calculation Example for Small Sample

Lecture 51 Criteria of a good Estimator

Lecture 52 Section PDF

Section 5: Hypothesis Testing

Lecture 53 Section Outline

Lecture 54 Business Example of Hypothesis Testing – part 01

Lecture 55 Business Example of Hypothesis Testing – part 02

Lecture 56 Introduction to Terms of Hypothesis Testing

Lecture 57 Steps of Hypothesis Testing

Lecture 58 Type I and II and Power of a test – part 01

Lecture 59 Type I and II and Power of a test – part 02

Lecture 60 Real Life Example of Type I and II error

Lecture 61 One and Tow Tail Tests

Lecture 62 P Value for I and II Tail Cases and Excel Computation

Lecture 63 Hypothesis Testing Examples 01

Lecture 64 Hypothesis Testing Examples 02

Lecture 65 Using MS Excel for Hypothesis Tests

Lecture 66 Section PDF

Section 6: Simple Linear Regression

Lecture 67 Section Outline

Lecture 68 Linear Relationship By Example

Lecture 69 Ordinary Least Square for Equation

Lecture 70 Understand Excel Chart Add Trendline Function

Lecture 71 Coefficient of determination

Lecture 72 Correlation Coefficient R

Lecture 73 Use of Linear Regression

Lecture 74 Linear Regression Using MS Excel Data Analysis Procedure

Lecture 75 Section PDF

Section 7: Categorical Data Analysis

Lecture 76 Section Overview

Lecture 77 Introduction to Categorical Variable

Lecture 78 Describe Categorical data one way

Lecture 79 Describe Categorical data two way

Lecture 80 Chi Square Statistic

Lecture 81 Feel The Chi Square Statistic

Lecture 82 Degree of freedom of a cross tab

Lecture 83 Chi Square Distribution

Lecture 84 Using Excel to conduct Chi Square Test

Lecture 85 dependent and independent variable

Lecture 86 statistical technique applicability at a glance

Lecture 87 Section PDF

Section 8: Analysis of Variance (ANOVA)

Lecture 88 Section Overview

Lecture 89 Scenario for ANOVA

Lecture 90 Clarity on ANOVA design

Lecture 91 Assumptions of ANOVA

Lecture 92 ANOVA Method at a Glance

Lecture 93 Calculate Between Sample Variance

Lecture 94 Calculate within Sample Variance

Lecture 95 Demo When Null Hypothesis Is True

Lecture 96 Intutive Understanding when samples are from different population

Lecture 97 F Statistics and F Distribution

Lecture 98 Conducting ANOVA using Excel

Lecture 99 Section PDF

Section 9: Non Parametric Tests

Lecture 100 Section Overview

Lecture 101 Scenario for Non Parametric Test

Lecture 102 Monotony vs Linearity

Lecture 103 Advantage n Disadvantage of Non Parametric Method

Lecture 104 Sign Test

Lecture 105 Mann Whitney U Test

Lecture 106 Kruskal Wallis H Test

Lecture 107 One Sample Run Test

Lecture 108 Spearman Rank Correlation

Lecture 109 Section PDF

Lecture 110 Closure Note

MBA Students,Statistics professionals,Statistics students,Analytics professionals,Data analytics folks,IT folks, Reporting Engineers who want to build their career into analytics or statistical analysis / market research

#### Course Information:

Udemy | English | 15h 42m | 1.19 GB
Created by: Gopal Prasad Malakar

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