Welcome to the session on ‘**Continuous Probability Distributions**’. In the last session, you learnt about **binomial distribution,** **uniform distribution** and **cumulative probability**.

## In this session

You will learn about **cumulative probability** in detail. You will see how the **probability of a continuous variable** is expressed and how it is different from the way the probability of a discrete variable is expressed. You will then learn about **normal distribution**, which is a commonly used probability distribution among continuous random variables.

## Prerequisites

There are no prerequisites for this session, other than, of course, knowledge of what was discussed in the previous two sessions.

## Guidelines for in-module questions

The in-video and in-content questions for this module are not graded. Note that graded questions are given on a separate page labelled ‘Graded Questions’ at the end of this session. These questions will adhere to the following guidelines:

First Attempt Marks | Second Attempt Marks | |

Questions with 2 Attempts | 10 | 5 |

Questions with 1 Attempt | 10 | 0 |

## People you will hear from in this session

**Subject Matter Expert**

#### Tricha Anjali

#### Associate Professor, IIIT- B

The International Institute of Information Technology, Bangalore, also known as IIIT-B, is one of India’s foremost graduate schools. Through its Integrated M.Tech., M.Tech., M.S. (Research) and PhD programs in the IT space, it focuses equally on innovation and education.

## Reference Ebook

Statistical Inference for Data Science by Brian Caffo.