Overview

Welcome to the Pre-MBA Statistics course! By the end of this course, you will be able to describe how statistics can be used to summarize, analyze, and interpret data. This course introduces you to some aspects of descriptive and inferential statistics. You will learn to distinguish between various data types and describe the operations that you can execute with each type of data and the right tools to use. The course also discusses the concepts of probability, which form the backbone of statistical analysis. In particular, the course explores how data behaves and provides insight into its analysis. Further, it discusses how data can be sampled and the pros and cons of these methods. The course also delves deeper into the behavior of large data sets based on well-established statistical results. This also enables you to identify the pitfalls of incorrectly using statistical laws. Lastly, you will learn how to estimate population parameters based on limited data and check the correctness of hypotheses about populations from limited data. This course is open to students from all disciplines holding a bachelor’s degree. A rudimentary knowledge of Mathematics would help grasp the concepts better.

Syllabus

Types of Data

In this module, you will learn about various types of data. You will gain insight into the types of data based on how they can be organized and the amount of inference possible from each of them. The module also analyzes the unique characteristics of diverse types of data. Lastly, you will also learn operations with usability and interpretability of various kinds of data.

Probability

In this module, you will learn about the basics of probability and the concept of random variables. This provides a relatively more formal approach to how data behaves and how uncertainties are modeled mathematically. Finally, the module discusses random variables and the special mathematical entities that model numerical data well and help in inferences.

Sampling

In this module, you will learn about different types of sampling methods used in surveys. Such sampling can be completely randomized or non-randomized. You will learn the pros and cons of these techniques and identify the right method to use in the situation you have in hand. You will also analyze the presentation of two important results: the law of large numbers and the central limit theorems.

Point and Interval Estimation

The task of collecting data from all members of a population is often expensive and sometimes impossible. You can, however, easily collect sample data from a population. In this module, you will learn to make inferences about the characteristics of the population from which you have collected sample data. In this module, you will learn about point estimation and then be able to construct a point estimate of the mean and standard deviation of data in the population. If the data you are interested in is expressed as a proportion, you can construct a point estimate of that proportion. The module also discusses interval estimation. You will learn how to build a confidence interval or a range around a point estimate so that you are appropriately confident that the population parameter will fall within that interval regardless of the sample from which the point estimate was obtained.

Hypothesis Testing

Given a sample of values and a claim that the sample comes from a population with certain characteristics, after going through this module, you will be able to construct tests that will justify or reject such a claim. You will learn the logic behind constructing and executing tests for means and proportions. You will also learn about tests to compare the properties of two populations based on samples from both populations.

Peer Review Assignment

This is a peer-review assignment based on the concepts taught in the Pre-MBA Statistics course. In this assignment, you will be able to apply the skills learned in the course in a realistic situation.