Statistics for Business – II
Statistics is a versatile discipline that has revolutionised the fields of business, engineering, medicine, and pure sciences. This IIMBx course Statistics for Business – II is Part 1 of the two-part series on Business Statistics and is ideal for learners who wish to enrol in business programs. These two courses cover topics in Descriptive Statistics. Spreadsheets containing real data from diverse areas such as economics, finance, and HR drive much of the discussions that are part of the course.
In the previous part part, Statistics for Business – I, you would have already learnt about the multiple ways to describe large datasets, numerically as well as visually, and also the tools and techniques available to solve problems.
In this part, Statistics for Business – II, the focus is on the use of the language of probability to examine the underlying distributions of random variables. You will model real-life phenomena using known variables such as Binomial, Poisson and Normal, and learn how to simulate data that are distributed according to these variables.
We shall take up datasets that have over a million rows which makes it difficult to analyse using a spreadsheet. This forms a natural setting for R, an advanced statistical programming platform. We will provide you with useful tutorials to get yourself acquainted with the R platform.
Week 1: Introduction to course
- Entrepreneurship Demo
Week 2: R Tutorial - I
- Installing R & RStudio
- Basics of RStudio
Week 3: R Tutorial - II
- Writing programs in RStudio & HR Demo
Week 4: Standard Variables - I
- Uniform Distribution
- Binomial Distribution
- Poisson Distribution
Week 5: Standard Variables - I
- Exponential Distribution
Week 6: Simulation
- Monte Carlo Simulation
Week 7: Normal Distribution
- Basic Application
After completing this two-part series, the participants should be able to:
- Analyse larger datasets using spreadsheets
- Create pertinent business questions to examine datasets with and derive answers
- Describe a random variable in probabilistic terms and derive parameters such as mean and variance
- Draw a simple random sample from a population
- Model business phenomena with known random variables such as Binomial, Poisson, and Normal
- Simulate variables that follow a prescribed distribution