Learn Probability concepts and counting techniques

Contains important data tables used for discrete probability distribution, conditional probabilities as well as a full layout of the deck of 52 cards.

In this lecture, we cover detailed explanations about the concept of a sample space in probability, sample points and events. These concepts are essential for solving probability problems.

In this video, we discuss Venn diagrams, the Union of events, the intersection of Events. Many examples are provided to fully illustrate the concepts.

In this lecture, we provide the definition of a probability using the relative frequency approach. Several examples about computing probabilities based on dices being rolled or coins being tossed are given. We conclude the video by showing all the cards in a deck of 52 cards and discuss at length the composition of the deck which is important for computing probabilities about cards being drawn from a deck of 52 cards.

In this video, we discuss the computation of basic probability about cards being selected from a deck of 52 cards. We discuss the addition rule of probability and solve related problems to ensure that the concept is well understood.

In this lecture, we cover the concept of conditional probability and Independent events. Solved problems are presented to explain in details how to solve conditional probabilities. We also cover the multiplication rule of probability and explain the concept with examples.

In this video, we illustrate through practical problems how to apply the addition rule of probability. We also demonstrate how to compute probabilities using the multiplicative rule.

In this lecture, we illustrate how to compute probabilities when cards are selected with and without replacement using the multiplicative rule of probabilities.

This lecture explains the fundamental principle of counting technique.

This lecture explains how to compute simple permutations of n elements using the factorial formula.

This lecture deals with the number of permutations where certain elements are duplicated or repeated.

In this video, we show how to compute the combination of n elements taken r at a time. Various exercises are solved in order to show in details how to calculate the number of combinations and how they are in practice to solve probability problems.

In this video, we talk about discrete random variables and the probability distribution of discrete random variable. We explain the conditions that must be satisfied for a probability distribution to be valid. In addition, we cover the concepts of the expected value of discrete random variables, the variance and standard deviation of the discrete random variable.

In this lecture, we talk about the Binomial distribution experiment an introduce important concepts about the distribution such as the failure and success probability, dichotomous outcomes and independent events.
The student can identify a binomial distribution problem after following this lecture.

In this video, we present the Binomial Distribution formula and discuss shortcuts that are commonly used when solving Binomial distributions problems.

In this lecture, we solve some Binomial distributions problems and illustrate how to use the distribution in practice.

This lecture illustrates how to compute probabilities using the Hypergeometric distribution. We show the differences between the Binomial distribution and the Hypergeometric distribution and use a practical example to explain how the distribution is used in solving probabilities.

We explain the Poisson distribution concepts and illustrate the computation of probabilities using the Poisson distribution with practical examples. We also show how the Poisson distribution can be used to approximate a Binomial distribution.

In this lecture we talk about the geometric distribution, a distribution that measures the number of trials until the first success.

This lecture explains the Geometric distribution which models the number of failures until the first success. Solved problems are used to illustrate how the Geometric probability distribution is used to solve problems.

We explain in detail The Negative Binomial distribution which is a discrete probability distribution that measures the number of failures until the rth success , where r >= 1. When r = 1, the Negative Binomial is the same as the geometric distribution. Exercises are used to explain how in practice the Negative Binomial distribution.