# How many types of random variable are available?

There are **three** types of random variables- discrete random variables, continuous random variables, and mixed random variables.

## How many types of random variables are there?

**two**types of random variables, i.e. discrete and continuous random variables.

## How many types of random variables are available Mcq?

**discrete or continuous**. The discrete random variable takes only certain values such as 1, 2, 3, etc., and a continuous random variable can take any value within a range such as the height of persons.

## What are the 2 types of random variables?

**discrete and continuous variables**. The main difference between the two categories is the type of possible values that each variable can take. In addition, the type of (random) variable implies the particular method of finding a probability distribution function.

## What is a random variable and explain types of random variable?

**discrete and continuous**.

## What is probability distribution in machine learning?

Probability distribution is **a function that gives the probabilities of occurrence of different possible outcomes for an experiment**.

## How do you identify discrete probability distribution?

How Do You Know If a Distribution Is Discrete? **If there are only a set array of possible outcomes (e.g. only zero or one, or only integers), then the data are discrete**.

## What is the basic elements of a language in AI?

Explanation: The basic element for a language is the **random variable**, which can be thought as a part of world and its status is initially unknown. 7. How many types of random variables are available? Explanation: The three types of random variables are boolean, discrete and continuous.

## What’s the difference between discrete and continuous data?

**Discrete data is the type of data that has clear spaces between values.** **Continuous data is data that falls in a constant sequence**. Discrete data is countable while continuous — measurable. To accurately represent discrete data, the bar graph is used.

## What is discrete in probability?

A discrete probability distribution can be defined as **a probability distribution giving the probability that a discrete random variable will have a specified value**. Such a distribution will represent data that has a finite countable number of outcomes.

## What is the difference between discrete and continuous variable?

Discrete and continuous variables are two types of quantitative variables: **Discrete variables represent counts (e.g. the number of objects in a collection).** **Continuous variables represent measurable amounts** (e.g. water volume or weight).

## How many types of distribution are there in statistics?

Based on the types of data we deal with, we have **two types of distribution functions**. For discrete data, we have discrete distributions; and for continuous data, we have continuous distributions.

## What are the machine learning models?

A machine learning model is **a file that has been trained to recognize certain types of patterns**. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.

## What are the categories of random variable?

Random variables are classified into **discrete and continuous variables**. The main difference between the two categories is the type of possible values that each variable can take. In addition, the type of (random) variable implies the particular method of finding a probability distribution function.

## How do you solve a random variable?

Step 1: List all simple events in sample space. Step 2: Find probability for each simple event. Step 3: List possible values for random variable X and identify the value for each simple event. Step 4: Find all simple events for which X = k, for each possible value k.

## How do you describe a continuous random variable?

A continuous random variable is **one which takes an infinite number of possible values**. Continuous random variables are usually measurements. Examples include height, weight, the amount of sugar in an orange, the time required to run a mile. A continuous random variable is not defined at specific values.

## What makes an artificial intelligence?

Artificial intelligence (AI) is the basis for **mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment**. Stated simply, AI is trying to make computers think and act like humans.

## How do you do natural language processing?

**Building an NLP Pipeline, Step-by-Step**

- Step 1: Sentence Segmentation. …
- Step 2: Word Tokenization. …
- Step 3: Predicting Parts of Speech for Each Token. …
- Step 4: Text Lemmatization. …
- Step 5: Identifying Stop Words. …
- Step 6: Dependency Parsing. …
- Step 6b: Finding Noun Phrases. …
- Step 7: Named Entity Recognition (NER)

## What is the mode of a PDF?

The mode or modal value of a continuous random variable X with a probability density function f(x) is the value of x for which f(x) takes a maximum value. Thus the mode is **the x-coordinate of the maximum point on the graph of f(x)**. The continuous random variable X has a pdf given by Find the mode of the distribution.

## How do we find the p value?

**To find the p value for your sample, do the following:**

- Identify the correct test statistic.
- Calculate the test statistic using the relevant properties of your sample.
- Specify the characteristics of the test statistic’s sampling distribution.
- Place your test statistic in the sampling distribution to find the p value.

## What is nominal data?

Nominal data is **data that can be labelled or classified into mutually exclusive categories within a variable**. These categories cannot be ordered in a meaningful way. For example, for the nominal variable of preferred mode of transportation, you may have the categories of car, bus, train, tram or bicycle.