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Jun 30, 2014 · In this video we help you learn what a random variable is, and the difference between discrete and continuous random variables. ...more. The idea of a random variable can be surprisingly difficult.
- 5 min
- 350.3K
- Dr Nic's Maths and Stats
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The normal, or Gaussian, distribution is the most common distribution in all of statistics. Here I explain the basics of how these distributions are created ...
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- StatQuest with Josh Starmer
Jan 24, 2018 · Welcome to Crash Course Statistics! In this series we're going to take a look at the important role statistics play in our everyday lives, because statistics are everywhere! Statistics help...
- 13 min
- 1.9M
- CrashCourse
Apr 26, 2023 · A distribution in statistics is a function that shows the possible values for a variable and how often they occur. Think about a die. It has six sides, numbered from 1 to 6.
- What Is A Probability Distribution?
- General Properties of Probability Distributions
- Discrete Probability Distributions
- Calculations For A Discrete Probability Distribution in A Table
- Types of Discrete Distributions
- Example Discrete Probability Distributions
- Continuous Probability Distributions
- How to Calculate Probabilities For Continuous Data
- Characteristics of Continuous Probability Distributions
- Example of The Normal Probability Distribution
A probability distribution is a statistical function that describes the likelihood of obtaining all possible values that a random variable can take. In other words, the values of the variable vary based on the underlying probability distribution. Typically, analysts display probability distributions in graphs and tables. There are equations to calc...
Statisticians refer to the variables that follow a probability distribution as random variables. The notation for random variables that follow a particular probability distribution function is the following: 1. X usually denotes random variables. 2. A tilde (~) indicates that it follows a distribution. 3. A capital letter signifies the distribution...
A discrete probability distribution can assume a discrete number of values. For example, coin tosses and counts of events are discrete functions. These are discrete distributions because there are no in-between values. For example, you can have only heads or tails in a coin toss. Similarly, if you’re counting the number of books that a library chec...
When you have a probability table, you can calculate the average outcome (i.e., the expected value) using the following procedures: 1. Multiply each outcome by its probability. 2. Sum those values Note that this procedure is a weighted averagethat uses the probabilities for the weights. For the number of cars example, we can take the table and calc...
There are a variety of discrete probability distributions that you can use to model different types of data. The correct discrete distribution depends on the properties of your data. For example, use the: 1. Binomial distribution to model binary data, such as coin tosses. 2. Poisson distribution to model count data, such as the count of library boo...
All the examples I include in this post will show you why I love to graph probability distributions. The case below comes from my blog post that presents a statistical analysis of flu shot effectiveness. I use the binomial probability distribution function to calculate the answer the question—how many times can I expect to catch the flu over 20 yea...
Continuous probability functions are also known as probability density functions. You know that you have a continuous distribution if the variable can assume an infinite number of values between any two values. Continuous variables are often measurements on a scale, such as height, weight, and temperature. Unlike discrete probability distributions ...
Probabilities for a continuous probability distribution are calculated over ranges of values rather than single points. A probability indicates the likelihood that a value will fall within an interval. This property is straightforward to demonstrate using a probability distribution plot—which we’ll get to soon! On a probability plot, the entire are...
Just as there are different types of discrete distributions for different kinds of discrete data, there are different probability distributions for continuous data. Each probability distribution has parameters that define its shape. Most distributions have between 1-3 parameters. Specifying these parameters establishes the shape of the distribution...
Let’s start off with the normal distribution to show how to use continuous probability distributions to calculate probabilities. The distribution of IQ scores is defined as a normal distribution with a mean of 100 and a standard deviation of 15. We’ll create the probability plot of this distribution. Additionally, let’s determine the likelihood tha...
machinelearningmastery.com · statistical-dataA Gentle Introduction to Statistical Data Distributions
Aug 8, 2019 · The distribution is a mathematical function that describes the relationship of observations of different heights. A distribution is simply a collection of data, or scores, on a variable. Usually, these scores are arranged in order from smallest to largest and then they can be presented graphically.
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Oct 23, 2020 · The normal distribution is a probability distribution, so the total area under the curve is always 1 or 100%. The formula for the normal probability density function looks fairly complicated. But to use it, you only need to know the population mean and standard deviation.
