**binomial, Poisson, Bernoulli, and multinomial**.

What are the main doctrines of mercantilism?

**what are 5 characteristics of mercantilism**.

### Contents

- Bernoulli Distribution. …
- Binomial Distribution. …
- Hypergeometric Distribution. …
- Negative Binomial Distribution. …
- Geometric Distribution. …
- Poisson Distribution. …
- Multinomial Distribution.

A discrete probability distribution function has two characteristics: **Each probability is between zero and one, inclusive**. The sum of the probabilities is one.

Discrete events are those with a finite number of outcomes, e.g. **tossing dice or coins**. For example, when we flip a coin, there are only two possible outcomes: heads or tails. When we roll a six-sided die, we can only obtain one of six possible outcomes, 1, 2, 3, 4, 5, or 6.

A discrete probability function is **a function that can take a discrete number of values (not necessarily finite)**. This is most often the non-negative integers or some subset of the non-negative integers. … The condition that the probabilities sum to one means that at least one of the values has to occur.

The most common discrete probability distributions include **binomial, Poisson, Bernoulli, and multinomial**.

The **5 discrete distributions** every Data Scientist should know.

What are the two requirements for a discrete probability distribution? The **first rule states that the sum of the probabilities must equal 1.** **The second rule states that each probability must be between 0 and 1, inclusive**. Determine whether the random variable is discrete or continuous.

Two key properties of discrete probability distributions: **The probability of each value x is a value between 0 and 1**. 2. The sum of the probabilities equals 1.

A random variable is discrete if it has a finite number of possible outcomes, or a countable number (i.e. the integers are infinite, but are able to be counted). … A discrete probability distribution **lists each possible value that a random variable can take, along with its probability**.

A **discrete distribution is one in which the data can only take on certain values**, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

Number of heads | Probability |
---|---|

0 | 0.25 |

1 | 0.50 |

2 | 0.25 |

The probability distribution of a discrete random variable can always be represented by a table. For example, **suppose you flip a coin two times**. This simple exercise can have four possible outcomes: HH, HT, TH, and TT. Now, let the variable X represent the number of heads that result from the coin flips.

Explanation: The main difference between normal distribution and binomial distribution is that **while binomial distribution is discrete**. This means that in binomial distribution there are no data points between any two data points. This is very different from a normal distribution which has continuous data points.

General Properties of Probability Distributions **The sum of all probabilities for all possible values must equal 1**. Furthermore, the probability for a particular value or range of values must be between 0 and 1. Probability distributions describe the dispersion of the values of a random variable.

discrete probability distribution. –**a listing of all the possible outcomes of an experiment for a discrete random variable**. -along with the relative frequency of each outcome or the probability of each outcome.

- Beta distribution,
- Cauchy distribution,
- Exponential distribution,
- Gamma distribution,
- Logistic distribution,
- Weibull distribution.

A chi-square distribution is **a continuous distribution** with degrees of freedom. It is used to describe the distribution of a sum of squared random variables.

Technically speaking, **age is a continuous variable** because it can take on any value with any number of decimal places. What is this? If you know someone’s birth date, you can calculate their exact age including years, months, weeks, days, hours, seconds, etc. so it’s possible to say that someone is 6.225549 years old.

If a variable can take on any value between two specified values, it is a continuous variable and the values follow a continuous distribution. However, **if the value can only take on a finite number of values**, the values fallow a discrete distribution.

What are the two requirements for a discrete probability distribution? The **first rule states that the sum of the probabilities must equal 1.** **The second rule states that each probability must be between 0 and 1, inclusive**. Determine whether the random variable is discrete or continuous.

A discrete probability distribution lists **each possible value a random variable can assume, together with its probability**.

If a random variable can take only a finite number of distinct values, then it must be discrete. Examples of discrete random variables include **the number of children in a family**, the Friday night attendance at a cinema, the number of patients in a doctor’s surgery, the number of defective light bulbs in a box of ten.

For a discrete random variable the expected value is **calculated by summing the product of the value of the random variable and its associated probability**, taken over all of the values of the random variable.

A discrete variable is a **variable whose value is obtained by counting**. … A discrete random variable X has a countable number of possible values. Example: Let X represent the sum of two dice.

For a discrete random variable X, the variance of X is obtained as follows: **var(X)=∑(x−μ)2pX(x)**, … For example, if X is a random variable measuring lengths in metres, then the standard deviation is in metres (m), while the variance is in square metres (m2).

The normal distribution is a **continuous probability distribution**.

Examples of discrete data include **the number of people in a class, test questions answered correctly, and home runs hit**. Tables, or information displayed in columns and rows, and graphs, or structured diagrams that display the relationship among variables using two axes, are two ways to display discrete data.

A discrete random variable has a countable number of possible values. The probability of each value of a discrete random variable is between 0 and 1, and **the sum of all the probabilities is equal to 1**.

Thus, in a discrete frequency distribution, **the values of the variable are determined individually**. The number of times each value occurs denotes the frequencies of the particular value or observation. Discrete frequency distribution is also known as ungrouped frequency distribution.

**6 Common** Probability Distributions every data science professional should know.

The most commonly used distribution is **the normal distribution**, which is used frequently in finance, investing, science, and engineering. The normal distribution is fully characterized by its mean and standard deviation, meaning the distribution is not skewed and does exhibit kurtosis.

In probability theory and statistics, the gamma distribution is a two-parameter family of **continuous probability distributions**.

A probability distribution may be either discrete or continuous. A discrete distribution means **that X can assume one of a countable (usually finite) number of values**, while a continuous distribution means that X can assume one of an infinite (uncountable) number of different values.

histogram if there were **millions** of measurements and a huge number of bins. histogram and a PDF is that a histogram involves discrete data (individual bins or classes), whereas a PDF involves continuous data (a smooth curve).