Cumulative Distribution Function
The Cumulative Distribution Function (CDF), of a real-valued random variable X, evaluated at x, is the probability function that X will take a value less than or equal to x. It is used to describe the probability distribution of random variables in a table. And with the help of these data, we can create a CDF plot in excel sheet easily.
A random variable is a variable that defines the possible outcome values of a random phenomenon. It is defined for both discrete and random variables.
In other words, CDF finds the cumulative probability for the given value. To determine the probability of a random variable, it is used and also to compare the probability between values under certain conditions. For discrete distribution functions, CDF gives the probability values till what we specify and for continuous distribution functions, it gives the area under the probability density function up to the given value specified.
CDF Definition And Formulas
The CDF defined for a discrete random variable is given as
Fx(x) = P(X≤x)
Where X is the probability that takes a value less than or equal to x and that lies in the semi-closed interval (a,b], where a < b.
Therefore the probability within the interval is written as
P(a < X ≤ b)=Fx(b)-Fx(a)
The CDF defined for a continuous random variable is given as;
Here, X is expressed in terms of integral of its probability density function fx.
In case, if the distribution of the random variable X has the discrete component at value b,
P(X=b)=Fx(b) – limx→bFx(x)
Cumulative Distribution Function Properties
The cumulative distribution function X(x) of a random variable has the following important properties:
- Every CDF Fx is non decreasing and right continuous
limx→-∞Fx(x) = limx→+∞Fx(x) = 1
- For all real numbers a and b with continuous random variable X, then the function fx is equal to the derivative of Fx, such that
This function is defined for all real values, sometimes it is defined implicitly rather than defining it explicitly. The CDF is an integral concept of PDF ( Probability Distribution Function )
Consider a simple example for CDF which is given by rolling a fair six-sided die, where X is the random variable
We know that the probability of rolling a six-sided die is given as:
Probability of getting 1 = P(X≤ 1 ) = 1 / 6
Probability of getting 2 = P(X≤ 2 ) = 2 / 6
Probability of getting 3 = P(X≤ 3 ) = 3 / 6
Probability of getting 4 = P(X≤ 4 ) = 4 / 6
Probability of getting 5 = P(X≤ 5 ) = 5 / 6
Probability of getting 6 = P(X≤ 6 ) = 6 / 6 = 1
From this, it is noted that the probability value always lies between 0 and 1 and it is non-decreasing and right continuous in nature.
Cumulative Frequency Distribution
The set of data which is represented in a tabular or graphical form, showing the frequency of observations occurring in a given interval is the frequency distribution. In case of cumulative frequency, the number of observations which occurs beyond any specific observation is calculated. To learn in details, visit the article for cumulative frequency distribution and understand the concept thoroughly with the help of examples.
The most important application of cumulative distribution function is used in statistical analysis. In statistical analysis, the concept of CDF is used in two ways.
- Finding the frequency of occurrence of values for the given phenomena using cumulative frequency analysis.
- To derive some simple statistics properties, by using empirical distribution function, that uses a formal direct estimate of CDFs.
The random variable with PDF is given by:
Find the cumulative distribution function(CDF)
Solution : The random variable with Probability Distribution Function is given to us. Let is find the CDF now;