In this topic, we will look at
- Different forms of data presentations and graphs
- Statistical measures
- Linear Correlation
- Probability and Compound Events
- Conditional Probability
- Discrete Random Variables
- Binomial Distribution
- Normal Distribution
- Continuous Random Variables
- Poisson Distribution
Probability Calculations
This page is aimed to help you with all the basics of probability. You will learn how to list outcomes, how to use Venn diagrams and probability trees. you will also find out about mutually exclusive events and independent events and get plenty of practice
Conditional Probability
You may well have tried the Monty Hall problem, a question based on the game show 'Let's Make a Deal'. In the game show, extra information is given about the whereabouts of a prize. If you use this extra information, you are more likely to choose the corre
Discrete Random Variables
On this page we will explore the concept of discrete random variables and their probability distributions. We will look at how we can display probability distributions in tables or as functions. The questions are not so complicated (it is just a way of for
Binomial Distribution
The Binomial distribution is an example of a discrete random variable. It has two parameters n (number of trials) and p (probability of success of one trial): X~B(n , p). For a situation to be described using a binomial model, the following must be true
Poisson Distribution
Although you might be able to model the number of fish in a pond using the Poisson distribution, the name Poisson comes from the French mathematician, Siméon Poisson from whom the distribution was named. The Poisson Distribution can model some discrete ran
Normal Distribution
The normal distribution is an extremely useful statistical distribution. Data for heights and weights of adults and objects produced by machines, for example, tend to follow the normal (bell-shaped) distribution. More importantly, if we take large enough s