## (R Tutorials for Citizen Data Scientist)

## Statistics with R for Business Analysts – Binomial Distribution

The binomial distribution model deals with finding the probability of success of an event which has only two possible outcomes in a series of experiments. For example, tossing of a coin always gives a head or a tail. The probability of finding exactly 3 heads in tossing a coin repeatedly for 10 times is estimated during the binomial distribution.

R has four in-built functions to generate binomial distribution. They are described below.

dbinom(x, size, prob) pbinom(x, size, prob) qbinom(p, size, prob) rbinom(n, size, prob)

Following is the description of the parameters used −

**x**is a vector of numbers.**p**is a vector of probabilities.**n**is number of observations.**size**is the number of trials.**prob**is the probability of success of each trial.

## dbinom()

This function gives the probability density distribution at each point.

# Create a sample of 50 numbers which are incremented by 1. x <- seq(0,50,by = 1) # Create the binomial distribution. y <- dbinom(x,50,0.5) # Give the chart file a name. png(file = "dbinom.png") # Plot the graph for this sample. plot(x,y) # Save the file. dev.off()

When we execute the above code, it produces the following result −

## pbinom()

This function gives the cumulative probability of an event. It is a single value representing the probability.

# Probability of getting 26 or less heads from a 51 tosses of a coin. x <- pbinom(26,51,0.5) print(x)

When we execute the above code, it produces the following result −

[1] 0.610116

## qbinom()

This function takes the probability value and gives a number whose cumulative value matches the probability value.

# How many heads will have a probability of 0.25 will come out when a coin # is tossed 51 times. x <- qbinom(0.25,51,1/2) print(x)

When we execute the above code, it produces the following result −

[1] 23

## rbinom()

This function generates required number of random values of given probability from a given sample.

# Find 8 random values from a sample of 150 with probability of 0.4. x <- rbinom(8,150,.4) print(x)

When we execute the above code, it produces the following result −

[1] 58 61 59 66 55 60 61 67

Applied Data Science Coding: How to get class distribution in Data

## Statistics with R for Business Analysts – Binomial Distribution

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**All Notebooks in One Bundle: Data Science Recipes and Examples in Python & R****. **

**End-to-End Python Machine Learning Recipes & Examples.**

**End-to-End R Machine Learning Recipes & Examples.**

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**

**R Machine Learning & Data Science Recipes: Learn by Coding**

**Comparing Different Machine Learning Algorithms in Python for Classification (FREE)**

There are 2000+ End-to-End Python & R Notebooks are available to build **Professional Portfolio as a Data Scientist and/or Machine Learning Specialist**. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.