Contents

- 1 Normal Distribution Probabilities using R
- 2 Normal Distribution
- 3 Normal probabilities using dnorm() function in R
- 4 Numerical Problem for Normal Distribution
- 5 Normal cumulative probability using pnorm() function in R
- 6 Normal Distribution Quantiles using qnorm() in R
- 7 Simulating Normal random variable using rnorm() function in R
- 8 Endnote

## Normal Distribution Probabilities using R

In this tutorial, you will learn about how to use `dnorm()`

, `pnorm()`

, `qnorm()`

and `rnorm()`

functions in R programming language to compute the individual probabilities, cumulative probabilities, quantiles and to generate random sample for Normal distribution.

Before we discuss R functions for Normal distribution, let us see what is Normal distribution.

## Normal Distribution

Normal distribution distribution is a continuous type probability distribution. Normal distribution has found applications in many fields.

A continuous random variable $X$ is said to have a normal distribution with parameters $\mu$ and $\sigma^2$ if its probability density function is given by

` $$ \begin{equation*} f(x;\mu, \sigma^2) = \left\{ \begin{array}{ll} \frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2\sigma^2}(x-\mu)^2}, & \hbox{$-\infty< x<\infty$,} \\ & \hbox{$-\infty<\mu<\infty$, $\sigma^2>0$;} \\ 0, & \hbox{Otherwise.} \end{array} \right. \end{equation*} $$ `

where $e= 2.71828...$ and $\pi = 3.1425926...$.

The parameter $\mu$ is called the location parameter (as it changes the location of density curve) and $\sigma^2$ is called the scale parameter of normal distribution (as it changes the scale of density curve).

In notation it can be written as $X\sim N(\mu,\sigma^2)$.

Read more about the theory and results of Normal distribution here.

## Normal probabilities using `dnorm()`

function in R

For continuous probability distribution, density is the value of the probability density function at $x$ (i.e., $f(x)$).

The syntax to compute the probability density function for Normal distribution using R is

`dnorm(x,mean=0, sd = 1)`

where

`x`

: the value(s) of the variable and,`mean`

: mean of Normal distribution (location parameter),`sd`

: standard deviation of Normal distribution (scale parameter).

The `dnorm()`

function gives the density for given value(s) `x`

, `mean`

and `sd`

.

## Numerical Problem for Normal Distribution

To understand the four functions `dnorm()`

, `pnorm()`

, `qnorm()`

and `rnorm()`

, let us take the following numerical problem.

### Normal Distribution Example

The GRE is widely used to help predict the performance of applicants to graduate schools. The range of possible scores on a GRE is 200 to 900. The psychology department at a university finds that the students in their department have scores with a mean of 544 and standard deviation of 103.

(a) Find the value of the density function at $x=550$.

(b) Plot the graph of Normal probability distribution.

(c) Find the probability that a student in psychology department has a score less than 480.

(d) Find the probability that a student in psychology department has a score at least 460.

(e) Find the probability that a student in the psychology department has a score between 480 and 730.

(f) Plot the graph of cumulative Normal probabilities.

(g) What is the value of $c$, if $P(X\leq c) \geq 0.80$?

(h) Simulate 1000 Normal distributed random variables with $\mu= 544$ and $\sigma = 103$.

Let $X$ denote the GRE score. Given that $X\sim Normal(544, 103^2)$.

### Example 1: How to use ` dnorm()`

function in R?

To find the value of the density function at $x=550$ we need to use ` dnorm()`

function.

First let us define the given parameters as

```
# mean of distribution
mu <- 544
# standard deviation of distribution
sigma <- 103
```

The probability density function of $X$ is

` $$ \begin{aligned} f(x)&= \frac{1}{103\sqrt{2\pi}}e^{-\frac{1}{2}\big(\frac{x-544}{103}\big)^2},\\ &\quad\text{for } x \geq 0. \end{aligned} $$ `

For part (a), we need to find the density function at $x=550$. That is $f(550)$.

(a) The value of the density function at $x=550$ is

` $$ \begin{aligned} f(550)&=\frac{1}{103\sqrt{2\pi}}e^{-\frac{1}{2}\big(\frac{550-544}{103}\big)^2}\\ &= 0.0038667 \end{aligned} $$ `

The above probability can be calculated using `dnorm(550,mean=544,sd=103)`

function in R.

```
# Compute Normal probability
result1 <- dnorm(550,mean=mu,sd=sigma)
result1
```

`[1] 0.00386666`

### Example 2 Visualize Normal probability distribution

Using `dnorm()`

function we can compute Normal distribution probabilities for given `x`

, `mean`

and `sd`

. To plot the probability density function of Normal distribution, we need to create a sequence of `x`

values and compute the corresponding probabilities.

```
# create a sequence of x values
x <- seq(200,900, by=10)
## Compute the Normal pdf for each x
px <- dnorm(x,mean=mu,sd=sigma)
```

(b) Visualizing Normal Distribution with `dnorm()`

function and `plot()`

function in R:

The probability density function of Normal distribution with given 544 and 103 can be visualized using `plot()`

function as follows:

```
## Plot the Normal probability dist
plot(x,px,type="l",xlim=c(200,900),ylim=c(0,max(px)),
lwd=3, col="darkred",ylab="f(x)",
main=expression(paste("PDF of Normal with ",
mu,"=544 and ",sigma,"=103")))
```

## Normal cumulative probability using `pnorm()`

function in R

The syntax to compute the cumulative probability distribution function (CDF) for Normal distribution using R is

`pnorm(q,mean=0, sd=1)`

where

`q`

: the value(s) of the variable,`mean`

: mean of Normal distribution (location parameter),`sd`

: standard deviation of Normal distribution (scale parameter).

Using this function one can calculate the cumulative distribution function of Normal distribution for given value(s) of `q`

(value of the variable `x`

), `mean`

and `sd`

.

### Example 3: How to use `pnorm()`

function in R?

In the above example, for part (c), we need to find the probability $P(X\leq 480)$.

(c) The probability that a student in the psychology department has a score less than 480 is

` $$ \begin{aligned} P(X < 480)&=P\bigg(\frac{X-\mu}{\sigma} < \frac{480-544}{103}\bigg)\\ &= P(Z < -0.621)\\ &=0.2671816 \end{aligned} $$ `

```
## Compute cumulative Normal probability
result2 <- pnorm(480,mean=mu,sd=sigma)
result2
```

`[1] 0.2671816`

### Example 4: How to use `pnorm()`

function in R?

In the above example, for part (d), we need to find the probability $P(X \geq 460)$.

(d) The probability that a student in psychology department has a score at least 460 is

` $$ \begin{aligned} P(X \geq 460) &= 1- P(X < 460)\\ &=1-P\bigg(\frac{X-\mu}{\sigma} < \frac{460-544}{103}\bigg)\\ &= 1- P(Z < -0.816)\\ &= 1- 0.2073834\\ &=0.7926166. \end{aligned} $$ `

To calculate the probability that a random variable $X$ is greater than a given number, one can use the option `lower.tail=FALSE`

in `pnorm()`

function.

Above probability can be calculated easily using `pnorm()`

function with argument `lower.tail=FALSE`

as

$P(X \geq 460)$= `pnorm(460,mean=544,sd=103,lower.tail=FALSE)`

or by using complementary event as

$P(X \geq 460) = 1- P(X\leq 460)$= 1- `pnorm(460,mean=544,sd=103)`

```
# compute cumulative Normal probabilities
# with lower.tail False
pnorm(460,mean=mu,sd=sigma,lower.tail=FALSE)
```

`[1] 0.7926166`

```
# Using complementary event
1-pnorm(460,mean=mu,sd=sigma)
```

`[1] 0.7926166`

### Example 5: How to use `pnorm()`

function in R?

One can also use `pnorm()`

function to calculate the probability that the random variable $X$ is between two values.

(e) The probability that a student in the psychology department has a score between $480$ and $730$ is

` $$ \begin{aligned} P(480 \leq X\leq 730) &=P(480 \leq X\leq 730)\\ &=P\bigg(\frac{480-544}{103}\leq \frac{X-\mu}{\sigma} \leq \frac{730-544}{103}\bigg)\\ &=P\bigg(-0.621 \leq Z \leq 1.806\bigg)\\ &= P(Z < 1.806) -P(Z < -0.621)\\ &=0.9645-0.2672\\ &= 0.6973 \end{aligned} $$ `

The above probability can be calculated using `pnorm()`

function as follows:

```
a <- pnorm(730,mean=mu,sd=sigma)
b <- pnorm(480,mean=mu,sd=sigma)
result3 <- a - b
result3
```

`[1] 0.6973455`

### Example 6: Visualize the cumulative Normal probability distribution

Using `pnorm()`

function we can compute Normal cumulative probabilities (CDF) for given `x`

, `mean`

and `sd`

. To plot the CDF of Normal distribution, we need to create a sequence of `x`

values and compute the corresponding cumulative probabilities.

```
# create a sequence of x values
x <- seq(200,900, by=10)
## Compute the Normal pdf for each x
Fx <- pnorm(x,mean=mu,sd=sigma)
```

(f) Visualizing Normal Distribution with `pnorm()`

function and `plot()`

function in R:

The cumulative probability distribution of Normal distribution with given `x`

, `mean`

and `sd`

can be visualized using `plot()`

function as follows:

```
## Plot the Normal probability dist
plot(x,Fx,type="l",xlim=c(200,900),ylim=c(0,1),
lwd=3, col="darkred",ylab="F(x)",
main=expression(paste("CDF of Normal with ",
mu,"=544 and ",sigma,"=103")))
```

## Normal Distribution Quantiles using `qnorm()`

in R

The syntax to compute the quantiles of Normal distribution using R is

`qnorm(p,mean=0,sd=1)`

where

`p`

: the value(s) of the probabilities,`mean`

: mean of Normal distribution (location parameter),`sd`

: standard deviation of Normal distribution (scale parameter).

The function `qnorm(p,mean,sd)`

gives $100*p^{th}$ quantile of Normal distribution for given value of `p`

, `mean`

and `sd`

.

The $p^{th}$ quantile is the smallest value of Normal random variable $X$ such that $P(X\leq x) \geq p$.

It is the inverse of `pnorm()`

function. That is, inverse cumulative probability distribution function for Normal distribution.

### Example 7: How to use `qnorm()`

function in R?

In part (g), we need to find the value of $c$ such a that $P(X\leq c) \geq 0.80$. That is we need to find the $80^{th}$ quantile of given Normal distribution.

```
mu <- 544
sigma <- 103
prob <- 0.80
```

```
# compute the quantile for Normal dist
qnorm(0.80,mean=mu, sd=sigma)
```

`[1] 630.687`

The $80^{th}$ percentile of given Normal distribution is 630.6869871.

### Visualize the quantiles of Normal Distribution

The quantiles of Normal distribution with given `p`

, `mean=mu`

and `sd=sigma`

can be visualized using `plot()`

function as follows:

```
p <- seq(0,1,by=0.01)
qx <- qnorm(p,mean=mu,sd=sigma)
# Plot the Quantiles of Normal dist
plot(p,qx,type="l",lwd=2,col="darkred",
ylab="quantiles",
main=expression(paste("Quantiles of Normal with ",
mu,"=544 and ",sigma,"=103")))
```

## Simulating Normal random variable using `rnorm()`

function in R

The general R function to generate random numbers from Normal distribution is

`rnorm(n,mean=0,sd=1)`

where,

`n`

: the sample observations,`mean`

: mean of Normal distribution (location parameter),`sd`

: standard deviation of Normal distribution (scale parameter).

The function `rnorm(n,mean,sd)`

generates `n`

random numbers from Normal distribution with given `mean`

and `sd`

.

### Example 8: How to use `rnorm()`

function in R?

In part (h), we need to generate 1000 random numbers from Normal distribution with given $mean = 544$ and $sd=103$.

(h) We can use `rnorm(1000,mean,sd)`

function to generate random numbers from Normal distribution.

```
## initialize sample size to generate
n <- 1000
# Simulate 1000 values From Normal dist
x_sim <- rnorm(n,mean=mu,sd=sigma)
```

The below graphs shows the density of the simulated random variables from Normal Distribution.

```
## Plot the simulated data
plot(density(x_sim),xlab="Simulated x",ylab="density",
lwd=5,col="darkred",
main=expression(paste("Simulated data Normal with ",
mu,"=544 and ",sigma,"=103")))
```

If you use same function again, R will generate another set of random numbers from $Normal(544,103^2)$.

```
# Simulate 1000 values From Normal dist
x_sim_2 <- rnorm(n,mean=mu,sd=sigma)
```

```
## Plot the simulated data
plot(density(x_sim_2),xlab="Simulated x",ylab="density",
lwd=5,col="blue",
main=expression(paste("Simulated data Normal with ",
mu,"=544 and ",sigma,"=103")))
```

For the simulation purpose to reproduce same set of random numbers, one can use `set.seed()`

function.

```
# set seed for reproducibility
set.seed(1457)
# Simulate 1000 values From Normal dist
x_sim_3 <- rnorm(n,mean=mu,sd=sigma)
```

```
## Plot the simulated data
plot(density(x_sim_3),xlab="Simulated x",ylab="density",
lwd=5,col="darkred",
main=expression(paste("Simulated data Normal with ",
mu,"=544 and ",sigma,"=103")))
```

```
set.seed(1457)
# Simulate 1000 values From Normal dist
x_sim_4 <- rnorm(n,mean=mu,sd=sigma)
```

```
## Plot the simulated data
plot(density(x_sim_4),xlab="Simulated x",ylab="density",
lwd=5,col="darkred",
main=expression(paste("Simulated data Normal with ",
mu,"=544 and ",sigma,"=103")))
```

Since we have used `set.seed(1457)`

function, R will generate the same set of Normal distributed random numbers.

```
hist(x_sim_4,breaks = 30,col="red4",
main=expression(paste("Histogram Normal with ",
mu,"=544 and ",sigma,"=103")))
```

To learn more about other discrete and continuous probability distributions using R, go through the following tutorials:

**Discrete Distributions Using R**

Binomial distribution in R

Poisson distribution in R

Geometric distribution in R

Negative Binomial distribution in R

Hypergeometric distribution in R

**Continuous Distributions Using R**

Uniform distribution in R

Exponential distribution in R

Log-Normal distribution in R

Beta distribution in R

Gamma distribution in R

Cauchy distribution in R

Laplace distribution in R

Logistic distribution in R

Weibull distribution in R

## Endnote

In this tutorial, you learned about how to compute the probabilities, cumulative probabilities and quantiles of Normal distribution in R programming. You also learned about how to simulate a Normal distribution using R programming.

To learn more about R code for discrete and continuous probability distributions, please refer to the following tutorials:

Probability Distributions using R

Let me know in the comments below, if you have any questions on Normal Distribution using R and your thought on this article.