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In previous sections, you learned about the fundamentals of descriptive statistics. Specifically, you learned about the properties of measures of central tendency and variability and how they describe the characteristics of distributions. Here, we’ll review the various distributions you will encounter in the probability theory as well as their properties.

Random variables in statistics are variables that can take on any value based on a probability distribution. As you learned in previous sections of this guide, probability distributions, like the normal distribution, tell you how typical a value is given measures like the mean and standard deviation.

Typical values are another way of saying values that are probable and improbable. The classic illustration of a random variable is the flip of a coin. Let's call the flip of a coin X, where the result, which can be either heads or tails, is what makes our random variable random. Here, the probability that it will be heads is 0.5, or 50%.

There are two types of numbers, as we discussed before: discrete and continuous variables. The differences between the two can be summarized in the table below.

 Discrete Continuous Definition Can take on a finite amount of possibilities Can take on an infinite amount of possibilities Example Colour: Blue, Red, Yellow Height: 1.69 cm, 1.692 cm, 1.693…..9cm Characteristics Mutually exclusive categories (can only be one or another, not both) Takes on a countable range Not mutually exclusive, can overlap between any two limits (in the previous example, 1.69 can have three “different” versions) Takes on an uncountable range

If a random variable is a discrete variable, it takes on a discrete distribution and, likewise, if a random variable is continuous, it takes on a continuous distribution.  The best Maths tutors available  4.9 (36 reviews)
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## Discrete Distributions

As we mentioned, random variables which are discrete take on discrete distributions. Distributions are nothing more than the probability of a random variable’s outcome. Therefore, a discrete distribution is nothing more than a discrete probability distribution. In other words, a discrete probability distribution displays each outcome of a random variable and its probability of occurring.

There are three fundamental discrete distributions: binomial, geometric and Poisson distributions.

## Binomial

Binomial distributions deal with binomial variables, which are variables that can take on only two values. Think of our example before, with our random variable X of flipping a coin. This X could take on only two values: heads and tails.

We can generalize any random variables with two outcomes as being either 0 or 1, where we assign each outcome either a 0 or a 1. The table below is a list of the most common binomial variables, also called Boolean variables.

 Outcome 1 Outcome 2 Heads Tails Yes No True False Success Failure 1 0

The binomial distribution can be written in the following format,

[

X backsim B(n,p)

]

This format is how all distributions are written, were a random variable is said to follow a distribution, signalled here by a “B,” and where and are the parameters. The parameter is the number of observations, or sample size, and p is the probability of either outcome 1 or outcome 2 occurring

Naturally, if was equal to the probability of outcome 1, then the probability for outcome 2 would be,

[

q = 1 - p

]

The image below shows binomial distributions for several and . ## Geometric

Geometric distributions deal with geometric sequences, which is where each number in the sequence can be found by multiplying the previous number by some fixed constant. This is because the geometric distribution is a probability distribution that indicates the probability of a certain amount of failures before the first success.

The table below illustrates the assumptions that need to be met about a variable in order to use a geometric distribution.

 Assumption Example The outcome of the random variable being studied is found after a series of independent trials The random variable being studied is people’s favourite ice cream flavour at a local ice cream shop There are only two outcomes for the random variable, either a failure or a success There are only two flavours, vanilla and chocolate, where we deem vanilla a “failure” and chocolate a “success” The probability for a success is the same in every independent trial The probability of someone liking vanilla is the same for every independent trial because one person preferring vanilla has no effect on whether the next person you ask likes vanilla

The geometric distribution is written as,

[

X backsim geom(p)

]

Below, you’ll find several geometric distributions with different . ## Poisson

Poisson distributions deal with the outcome of a random variable within a specific time frame, where it depicts the probability for each number of times an outcome can occur within that time interval. Below, you can find the assumptions for a Poisson distribution.

 Assumption Example The number of times an event occurs can take on only integer values , the number of times an outcome occurs, can only = 0, 1, 5, 100, etc. For example, the number of times someone calls customer service The outcomes are independent One person choosing to call customer service does not affect another person choosing to call customer service Outcomes cannot happen at the exact same time Think about it - even if you place a call at the same time as another customer, there will be milliseconds of difference between the time they connect to customer service

Poisson distributions are written as,

[

X backsim Pois(mu)

]

Where the mean, , is the average number of events occurring in the interval given by,

[

mu = lambda t

]

Below are Poisson distributions with different  Discrete Distributions Binomial Geometric Poisson Deals with Boolean variables: ones that only take on two values Variables in a geometric sequence, where the probability is of the first “success” Indicates the probability of an event occurring a certain amount of times in a given time frame. Example: Flipping a coin, can only be either heads or tails Example: Polling people at an ice cream stand about favourite flavours until the first chocolate is found Example: How many people call customer service within an hour interval [ X backsim B(n,p) ] [ X backsim geom(p) ] [ X backsim Pois(mu) ]

## Continuous Distributions

As the name suggests, continuous distributions display the probability of continuous random variables. These can be variables like heights, test scores, speed, etc. While there are a couple of basic distributions, the most important one to know is the normal or standard normal distribution.

## Normal

As we’ve showed you before, the normal distribution models normal variables, which follow the 68-95-99.7 rule. The properties for a normal distribution are listed in the table below.

 Property Explanation Shape A normal probability distribution is shaped like a bell curve, which is why it’s often called a “bell curve” Distribution Its distribution is symmetric around the mean, which means the two sides before and after the mean mirror each other Rule 68% of the data falls within 1 of the mean 95% of the data falls within 2 99.7% fall within 3 It is written as,

[

X backsim N(mu, sigma)

]

Below are normal distributions with different . ## Standard Normal

A standard normal distribution is the same as a normal distribution. The only difference is that the data points are standardized using the standardization, or z-score, formula.

[

A = frac{X-mu}{sigma}

]

It is written as,

[

X backsim N(0, 1)

]

Below is a standard normal distribution. Notice that the mean is always at 0. Continuous Distributions Normal Standard Normal Models continuous variables with a normal distribution Models continuous variables that have been standardized [ X backsim N(mu, sigma) ] [ X backsim N(0, 1) ] The platform that connects tutors and students     4.00 (2 rating(s)) Loading... 