Probability for Risk Management and Optimization

Some examples of very simple probability questions. 

This video takes a simple example to explain the concept of conditional probability.

This video takes a question related to permutation and explains it's basic concept.

This video discusses a simple example of combination and also explains the main difference between permutation and combination.

This video discusses an intuitive way to find probability of a specific person being part of a committee formed from a group of persons. This uses the concept of combination and probability.  

This video finds the probability of a specific person being selected for a specific position. The video finds the permutations in related situations to compute the probability. However, the question can be directly solved by finding the probability of a person being selected from a group of persons. 

Probability Question 1 video had solved similar question but the situation was related to combination. Learners are suggested to use the probability of a person being selected for a position in the committee to find the probability asked in the question. Relate the answers to have a smart insight into these probability concepts. 

This video takes another dimension in counting and discusses the concept using a probability question.

All the outcomes of an experiment may not be equally likely. This is obvious. But an inclination to assume the outcomes as equally likely is a common source of error in probability application. This video discusses about it with few examples. 

Continuous variables creates infinite number of possibilities in smallest of the ranges of data values. This makes probability computation different than that with discrete variables. This video takes an example of continuous variable and explains the approach to handle probability questions in such situations.

Probability changes with the variable. This video is an introduction to probability distribution concept.

This lecture discusses the dependence or independence of outcomes of different trials in an experiment. It is very important point to check in identifying the right probability distribution that describes the given situation.

This is an intuitive discussion about Binomial Distribution.

It discusses about conditions under which binomial distribution can be applied and talks about various components of binomial term.

This lecture takes another example of binomial distribution application.

Questions given in this lecture don't require any computation. Apply your concept and try to answer them. It makes you think about Binomial Distribution in somewhat smart way. Answer will be published shortly in next lecture. Meanwhile you can ask questions to handle your curiosity.

Four questions are solved here. Few situations related to counting and it's use in probability are discussed. Few questions are related to data requirement.