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Statistics By Jim

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Probability

Beta Distribution: Uses, Parameters & Examples

By Jim Frost Leave a Comment

The beta distribution is a continuous probability distribution that models random variables with values falling inside a finite interval. Use it to model subject areas with both an upper and lower bound for possible values. Analysts commonly use it to model the time to complete a task, the distribution of order statistics, and the prior distribution for binomial proportions in Bayesian analysis. [Read more…] about Beta Distribution: Uses, Parameters & Examples

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Geometric Distribution: Uses, Calculator & Formula

By Jim Frost Leave a Comment

What is a Geometric Distribution?

The geometric distribution is a discrete probability distribution that calculates the probability of the first success occurring during a specific trial. In other words, during a series of attempts, what is the probability of success first occurring during each attempt? Use this distribution when you need to understand how many attempts are necessary to produce the first successful outcome. [Read more…] about Geometric Distribution: Uses, Calculator & Formula

Filed Under: Probability Tagged With: distributions, graphs

Permutation vs Combination: Differences & Examples

By Jim Frost 3 Comments

In mathematics and statistics, permutations vs combinations are two different ways to take a set of items or options and create subsets. For example, if you have ten people, how many subsets of three can you make? While permutation and combination seem like synonyms in everyday language, they have distinct definitions mathematically.

  • Permutations: The order of outcomes matters.
  • Combinations: The order does not matter.

Let’s understand this difference between permutation vs combination in greater detail. And then you’ll learn how to calculate the total number of each. [Read more…] about Permutation vs Combination: Differences & Examples

Filed Under: Probability Tagged With: conceptual

Binomial Distribution: Uses, Calculator & Formula

By Jim Frost Leave a Comment

What is a Binomial Distribution?

The binomial distribution is a discrete probability distribution that calculates the probability an event will occur a specific number of times in a set number of opportunities. Use the binomial distribution when your outcome is binary. Binary outcomes have only two possible values that are mutually exclusive. [Read more…] about Binomial Distribution: Uses, Calculator & Formula

Filed Under: Probability Tagged With: distributions, graphs

Odds Ratio: Formula, Calculating & Interpreting

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What is an Odds Ratio?

An odds ratio (OR) calculates the relationship between a variable and the likelihood of an event occurring. A common interpretation for odds ratios is identifying risk factors by assessing the relationship between exposure to a risk factor and a medical outcome. For example, is there an association between exposure to a chemical and a disease? [Read more…] about Odds Ratio: Formula, Calculating & Interpreting

Filed Under: Probability Tagged With: conceptual, interpreting results

Lognormal Distribution: Uses, Parameters & Examples

By Jim Frost 1 Comment

What is the Lognormal Distribution?

The lognormal distribution is a continuous probability distribution that models right-skewed data. The shape of the lognormal distribution is comparable to the Weibull and loglogistic distributions. [Read more…] about Lognormal Distribution: Uses, Parameters & Examples

Filed Under: Probability Tagged With: distributions, graphs

A Statistical Thanksgiving: Global Income Distributions

By Jim Frost 6 Comments

In the United States, our Thanksgiving holiday is fast approaching. On this day, we give thanks for the good things in our lives.

Painting of the first Thanksgiving by Brownscombe.For this post, I wanted to quantify how thankful we should be. Ideally, I’d quantify something truly meaningful, like happiness. Unfortunately, most countries are not like Bhutan, which measures the gross national happiness and incorporates it into their five-year development plans.

Instead, I’ll focus on something that is more concrete and regularly measured around the world—income. By examining income distributions, I’ll show that you have much to be thankful for, and so does most of the world! [Read more…] about A Statistical Thanksgiving: Global Income Distributions

Filed Under: Probability Tagged With: distributions, graphs

Uniform Distribution: Definition & Examples

By Jim Frost 4 Comments

What is a Uniform Distribution?

The uniform distribution is a symmetric probability distribution where all outcomes have an equal likelihood of occurring. All values in the distribution have a constant probability, making them uniformly distributed. This distribution is also known as the rectangular distribution because of its shape in probability distribution plots, as I’ll show you below. [Read more…] about Uniform Distribution: Definition & Examples

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Conditional Probability: Definition, Formula & Examples

By Jim Frost 4 Comments

What is Conditional Probability?

A conditional probability is the likelihood of an event occurring given that another event has already happened. Conditional probabilities allow you to evaluate how prior information affects probabilities. For example, what is the probability of A given B has occurred? When you incorporate existing facts into the calculations, it can change the likelihood of an outcome. [Read more…] about Conditional Probability: Definition, Formula & Examples

Filed Under: Probability Tagged With: analysis example, conceptual

Empirical Rule: Definition & Formula

By Jim Frost 1 Comment

What is the Empirical Rule?

The empirical rule in statistics, also known as the 68 95 99 rule, states that for normal distributions, 68% of observed data points will lie inside one standard deviation of the mean, 95% will fall within two standard deviations, and 99.7% will occur within three standard deviations. [Read more…] about Empirical Rule: Definition & Formula

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Gamma Distribution: Uses, Parameters & Examples

By Jim Frost 11 Comments

What is the Gamma Distribution?

The gamma distribution is a continuous probability distribution that models right-skewed data. Statisticians have used this distribution to model cancer rates, insurance claims, and rainfall. Additionally, the gamma distribution is similar to the exponential distribution, and you can use it to model the same types of phenomena: failure times, wait times, service times, etc. [Read more…] about Gamma Distribution: Uses, Parameters & Examples

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Exponential Distribution: Uses, Parameters & Examples

By Jim Frost 6 Comments

What is the Exponential Distribution?

The exponential distribution is a right-skewed continuous probability distribution that models variables in which small values occur more frequently than higher values. Small values have relatively high probabilities, which consistently decline as data values increase. Statisticians use the exponential distribution to model the amount of change in people’s pockets, the length of phone calls, and sales totals for customers. In all these cases, small values are more likely than larger values. [Read more…] about Exponential Distribution: Uses, Parameters & Examples

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Weibull Distribution: Uses, Parameters & Examples

By Jim Frost 5 Comments

What is a Weibull Distribution?

The Weibull distribution is a continuous probability distribution that can fit an extensive range of distribution shapes. Like the normal distribution, the Weibull distribution describes the probabilities associated with continuous data. However, unlike the normal distribution, it can also model skewed data. In fact, its extreme flexibility allows it to model both left- and right-skewed data. [Read more…] about Weibull Distribution: Uses, Parameters & Examples

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Poisson Distribution: Definition & Uses

By Jim Frost 11 Comments

What is the Poisson Distribution?

The Poisson distribution is a discrete probability distribution that describes probabilities for counts of events that occur in a specified observation space. It is named after Siméon Denis Poisson.

In statistics, count data represent the number of events or characteristics over a given length of time, area, volume, etc. For example, you can count the number of cigarettes smoked per day, meteors seen per hour, the number of defects in a batch, and the occurrence of a particular crime by county. [Read more…] about Poisson Distribution: Definition & Uses

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Using Combinations to Calculate Probabilities

By Jim Frost 6 Comments

Combinations in probability theory and other areas of mathematics refer to a sequence of outcomes where the order does not matter. For example, when you’re ordering a pizza, it doesn’t matter whether you order it with ham, mushrooms, and olives or olives, mushrooms, and ham. You’re getting the same pizza! [Read more…] about Using Combinations to Calculate Probabilities

Filed Under: Probability Tagged With: analysis example, choosing analysis, conceptual

Using Permutations to Calculate Probabilities

By Jim Frost 7 Comments

Permutations in probability theory and other branches of mathematics refer to sequences of outcomes where the order matters. For example, 9-6-8-4 is a permutation of a four-digit PIN because the order of numbers is crucial. When calculating probabilities, it’s frequently necessary to calculate the number of possible permutations to determine an event’s probability.

In this post, I explain permutations and show how to calculate the number of permutations both with repetition and without repetition. Finally, we’ll work through a step-by-step example problem that uses permutations to calculate a probability. [Read more…] about Using Permutations to Calculate Probabilities

Filed Under: Probability Tagged With: analysis example, choosing analysis, conceptual

Multiplication Rule for Calculating Probabilities

By Jim Frost 7 Comments

The multiplication rule in probability allows you to calculate the probability of multiple events occurring together using known probabilities of those events individually. There are two forms of this rule, the specific and general multiplication rules.

In this post, learn about when and how to use both the specific and general multiplication rules. Additionally, I’ll use and explain the standard notation for probabilities throughout, helping you learn how to interpret it. We’ll work through several example problems so you can see them in action. There’s even a bonus problem at the end! [Read more…] about Multiplication Rule for Calculating Probabilities

Filed Under: Probability Tagged With: analysis example, choosing analysis, conceptual

Using Contingency Tables to Calculate Probabilities

By Jim Frost 18 Comments

Contingency tables are a great way to classify outcomes and calculate different types of probabilities. These tables contain rows and columns that display bivariate frequencies of categorical data. Analysts also refer to contingency tables as crosstabulation (cross tabs), two-way tables, and frequency tables.

Statisticians use contingency tables for a variety of reasons. I love these tables because they both organize your data and allow you to answer a diverse set of questions. In this post, I focus on using them to calculate different types of probabilities. These probabilities include joint, marginal, and conditional probabilities. [Read more…] about Using Contingency Tables to Calculate Probabilities

Filed Under: Probability Tagged With: analysis example, conceptual

Probability Definition and Fundamentals

By Jim Frost 10 Comments

What is Probability?

The definition of probability is the likelihood of an event happening. Probability theory analyzes the chances of events occurring. You can think of probabilities as being the following:

  • The long-term proportion of times an event occurs during a random process.
  • The propensity for a particular outcome to occur.

Common terms for describing probabilities include likelihood, chances, and odds. [Read more…] about Probability Definition and Fundamentals

Filed Under: Probability Tagged With: conceptual

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