Statistics for MBA Business statistics explained by example

Statistics for MBA/ Business statistics explained by example, available at $64.99, has an average rating of 4.5, with 110 lectures, 9 quizzes, based on 386 reviews, and has 2471 subscribers.

You will learn about 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 This course is ideal for individuals who are MBA Students or Statistics professionals or Statistics students or Analytics professionals or Data analytics folks or IT folks, Reporting Engineers who want to build their career into analytics or statistical analysis / market research It is particularly useful for MBA Students or Statistics professionals or Statistics students or Analytics professionals or Data analytics folks or IT folks, Reporting Engineers who want to build their career into analytics or statistical analysis / market research.

Enroll now: Statistics for MBA/ Business statistics explained by example

Summary

Title: Statistics for MBA/ Business statistics explained by example

Price: $64.99

Average Rating: 4.5

Number of Lectures: 110

Number of Quizzes: 9

Number of Published Lectures: 110

Number of Published Quizzes: 9

Number of Curriculum Items: 119

Number of Published Curriculum Objects: 119

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

Most of the students of MBA (Master of business administration program) / machine learning program / computer science program hate the introductory statistics / business statistics course. The reason is that most of the instructor explain the concept in such a way that students are hardly able to relate to concept with real life situation. Hence the course becomes a nightmare for students and they look forward for just completion of semester to  get rid of the same.

That’s why this course has been prepared through simulation and real life examples.

This course covers the entire syllabus of most of the business statistics / introductory statistics course of MBA (Master of Business administration) program. The explanations are so simple and intuitive that you will learn statistics for life and will love the subject. 

I recommend you to explore the course.

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

Course Curriculum

Chapter 1: Probability and Expectations

Lecture 1: Welcome Note

Lecture 2: Section Overview

Lecture 3: Relative Frequency and Probability with Excel simulation – view to understand it

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

Chapter 2: Central Tendencies and Dispersion

Lecture 1: Section Overview

Lecture 2: Arithmetic Mean

Lecture 3: Advantage n Disadvantage of Arithmatic Mean

Lecture 4: Geometric Mean and Its applicability

Lecture 5: Weighted Mean

Lecture 6: Median and Its Calculations

Lecture 7: Advantage and Applicability of Median

Lecture 8: Mode Its Advantage and Usage

Lecture 9: Dispersion: Why you shd Know

Lecture 10: Range and Its Advantage and Disadvantage

Lecture 11: Average Absolute Difference

Lecture 12: Variance and Standard deviation

Lecture 13: Note: Square Error Is Minimum around Mean

Lecture 14: Coefficient of Variance and Z statistics

Lecture 15: Exercise – caculate central tenedencies, dispersion etc.

Lecture 16: Section PDF

Chapter 3: Central Limit Theorem

Lecture 1: Section outline

Lecture 2: Frequency Distribution

Lecture 3: Normal Distribution and its properties

Lecture 4: Real Life Example of Normal distribution

Lecture 5: Normal distribution due to aggregation

Lecture 6: CLT concepts and demo

Lecture 7: Validate properties of Normal Distribution

Lecture 8: Section PDF

Chapter 4: Sampling Distribution

Lecture 1: Section outline

Lecture 2: Terms Associated with Sampling Distribution

Lecture 3: Examples of Sample Statistic

Lecture 4: Sampling distribution of Means

Lecture 5: Sampling Distribution of proportion

Lecture 6: Optional topic – Sampling distribution of means and proportions with IID series

Lecture 7: Point Estimate and Interval Estimate

Lecture 8: Intuitive Understanding and Demo of confidence Interval

Lecture 9: Formal defintions and table for confidence interval

Lecture 10: Calculation example of confidence interval for sample proportions

Lecture 11: Confidence Interval for Mean

Lecture 12: Demo of confidence Interval for Mean

Lecture 13: Example of Confidence Interval Calculation

Lecture 14: Preamble for small sample statistic

Lecture 15: Demo of T Distribution

Lecture 16: Confidence Interval Calculation Example for Small Sample

Lecture 17: Criteria of a good Estimator

Lecture 18: Section PDF

Chapter 5: Hypothesis Testing

Lecture 1: Section Outline

Lecture 2: Business Example of Hypothesis Testing – part 01

Lecture 3: Business Example of Hypothesis Testing – part 02

Lecture 4: Introduction to Terms of Hypothesis Testing

Lecture 5: Steps of Hypothesis Testing

Lecture 6: Type I and II and Power of a test – part 01

Lecture 7: Type I and II and Power of a test – part 02

Lecture 8: Real Life Example of Type I and II error

Lecture 9: One and Tow Tail Tests

Lecture 10: P Value for I and II Tail Cases and Excel Computation

Lecture 11: Hypothesis Testing Examples 01

Lecture 12: Hypothesis Testing Examples 02

Lecture 13: Using MS Excel for Hypothesis Tests

Lecture 14: Section PDF

Chapter 6: Simple Linear Regression

Lecture 1: Section Outline

Lecture 2: Linear Relationship By Example

Lecture 3: Ordinary Least Square for Equation

Lecture 4: Understand Excel Chart Add Trendline Function

Lecture 5: Coefficient of determination

Lecture 6: Correlation Coefficient R

Lecture 7: Use of Linear Regression

Lecture 8: Linear Regression Using MS Excel Data Analysis Procedure

Lecture 9: Section PDF

Chapter 7: Categorical Data Analysis

Lecture 1: Section Overview

Lecture 2: Introduction to Categorical Variable

Lecture 3: Describe Categorical data one way

Lecture 4: Describe Categorical data two way

Lecture 5: Chi Square Statistic

Lecture 6: Feel The Chi Square Statistic

Lecture 7: Degree of freedom of a cross tab

Lecture 8: Chi Square Distribution

Lecture 9: Using Excel to conduct Chi Square Test

Lecture 10: dependent and independent variable

Lecture 11: statistical technique applicability at a glance

Instructors

Gopal Prasad Malakar
Trains Industry Practices on data science / machine learning

Rating Distribution

1 stars: 10 votes

2 stars: 17 votes

3 stars: 66 votes

4 stars: 117 votes

5 stars: 176 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!