Continuous Random Variable

A continuous random variable has an infinite number of possible values within a certain range. In statistics and probability theory, a continuous random variable plays a crucial role where the results are measured instead of being counted. A random variable is considered continuous if it can take any value within a specific interval. It is characterized by its possible values, which are infinite or uncountable. Also, using a function known as the probability density function, we can determine if a probability will fall inside a specific interval.

To understand the probability distributions and conduct statistical analysis, we need to comprehend the various properties of a continuous random variable. Several important properties distinguish the continuous random variable from the discrete random variable. They include: 

Probability Density Function

A probability density function f(x) defines a continuous random variable x. The relative probability for x to be a certain value x is represented by the PDF f(x). The PDF needs to meet two conditions. The first one is f(x) ≥ 0 for all  x. It means the probability density function (PDF) must always be non-negative but can be zero for some values. The next condition is that the total probability over all possible values of x must be 1, meaning the sum of all probability values must be 100% or 1. This is written as:
  

\(\int_{-\infty}^{\infty} f(x)\, dx = 1 \)

Cumulative Distribution Function (CDF)

The cumulative distribution function (CDF), represented as F(x), shows the likelihood that a continuous random variable X will take a value that is less than or equal to x: 

\(F(x) = P(X \leq x) = \int_{-\infty}^{x} f(t)\,dt \)

The CDF is non-decreasing and continuous. The CDF approaches 0 as x moves closer to negative infinity.

lim ​F(x) = 0
x→∞

Likewise, the CDF approaches 1 as x moves closer to positive infinity.  

lim ​F(x) = 1
x→∞ 

Moment Generating Function (MGF)

The moment generating function (MGF) of a continuous random variable X is represented as Mx (t). It is defined as:

\(M_X(t) = E\!\left(e^{tX}\right) = \int_{-\infty}^{\infty} e^{tx} f(x)\,dx \)

The MGF can be used to find all the moments of X, including its mean and variance if it exists.

Characteristic Function

The characteristic function of a continuous random variable X is expressed as ϕX(t). It is the Fourier transform of the probability density function (PDF). 

\(\varphi_X(t) = E\!\left(e^{itX}\right) = \int_{-\infty}^{\infty} e^{itx} f(x)\,dx\)

It determines the distribution of X uniquely.

Mean and Variance of Continuous Random Variable

A continuous random variable X with PDF f(x) has the following expectation or mean:

\(E(X) = \int_{-\infty}^{\infty} x f(x)\,dx \)

The formula represents the expected value of X. 

The variance of X measures the average of the squared deviations of the random variable from the mean. It is defined as:

\(\operatorname{Var}(X) = E\!\left[(X - E(X))^{2}\right] = \int_{-\infty}^{\infty} (x - \mu)^{2} f(x)\,dx \)

Here, μ = E (X) is the mean. 

Where X's second moment is represented by E(X²) and it is denoted as:

\(E(X^2) = \int_{-\infty}^{\infty} x^2 f(x)\,dx \)

The probabilities associated with a continuous random variable are described by the probability density function (PDF) and cumulative probability function (CDF). Here are some key formulas related to continuous random variables. 

PDF of Continuous Random Variable

PDF of a continuous random variable is a function that shows the likelihood of the variable taking on different values. It does not provide probabilities for specific values but rather for intervals. X is the continuous random variable, and the formula for the PDF, f(x), is:

f(x) = dF(x) / dx = F'(x)

Here, F(x) is the cumulative distribution function. 

CDF of Continuous Random Variable

It describes the likelihood that a specific value, x, will be equal to or less than the random variable, X. CDF of a continuous random variable can be found by integrating the PDF. The formula is calculated between two points, a and b. The formula for CDF of continuous random variable is: 

\(P(a < X \leq b) = F(b) - F(a) = \int_{a}^{b} f(x)\,dx \)

The Mean of Continuous Random Variable

The mean of the continuous random variable, X, is its expected value. It is calculated as a weighted average of all possible values. Also, each value is weighted according to the probability density function (PDF). The formula is:

\(E[X] = \mu = \int_{-\infty}^{\infty} x f(x)\,dx \)

The Variance of Continuous Random Variable

It measures how much the values of X deviate from the mean. It is the expected value of the squared differences between the variable and its mean. The formula is given as follows:

\(\operatorname{Var}(X) = \sigma^{2} = \int_{-\infty}^{\infty} (x - \mu)^{2} f(x)\,dx \)

A random experiment’s numerical outcome is called a random variable. The two types of random variables are discrete random variable and continuous random variable. Let us look at the differences between them:

We use continuous random variables to model situations that involve measurements. For example, it is used when we want to know the possible amount of rainfall over a year or temperature on any given day. Following are the significant continuous random variables linked to certain probability distributions: 

Uniform Random Variable

A uniform random variable represents a uniform distribution, which describes events with equal chances of happening. A uniform random variable’s PDF is as follows: 

\(f(x) = \begin{cases} \dfrac{1}{b - a}, & a \le x \le b, \\ 0, & \text{otherwise.} \end{cases}\)

Here, a and b are the lower and upper bounds of the distribution. 
 

Normal Random Variable

A normal random variable is a continuous random variable that is used to model a normal distribution. If a normal distribution’s parameters are expressed as X ~ N (μ, σ²), then the following is the formula for the PDF:

\(f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{1}{2}\left(\frac{x - \mu}{\sigma}\right)^2}\)

Here, μ is the mean 

σ is the standard deviation 

σ² is the variance
 

Exponential Random Variable

An exponential distribution is a continuous probability distribution used to model processes in which a specific number of events occur continuously and independently at a constant average rate λ, where λ ≥ 0. The exponential random variable follows an exponential distribution. The following is the PDF of an exponential random variable:

\(f(x) = \begin{cases} \lambda e^{-\lambda x}, & x \ge 0, \\ 0, & x < 0. \end{cases} \)

Where,

\(\lambda \) > 0 is the rate parameter

x \(\ge\ \)0 because the exponential distribution models non-negative values

Within a specified range, a continuous random variable can have an infinite number of values. This concept is widely used in situations where measurements are involved. Here are some real-world applications of continuous random variables. 

• Weather forecasters employ continuous random variable to model temperature, wind speed, rainfall, and humidity. For instance, the probability of rainfall within a certain range is calculated by using normal distribution. 

• The values that fluctuate continuously like stock prices, interest rates, and exchange rates can be modeled by log-normal distribution. 

• To assess the variations in production and manufacturing, we can use the Gaussian distribution. Also, variables like weight, length, or thickness of products follow a continuous distribution.

• Continuous variables in transportation, including travel time, vehicle speed, and fuel consumption, are used in urban planning and traffic management.