Discrete Random Variable And Continous Random Variable

Discrete Random Variables A probability distribution for a discrete r.v. X consists of - Possible values x 1, x 2, . . . , x n - Corresponding probabilities p 1, p 2, . . . , p n A continuous random variable can take any value in some interval Example X time a customer spends waiting in line at the store

To understand what discrete, continuous, and random variables are, you first need to know what a variable is. In math, a variable is a quantity that can take on different values. It is a quantity that quotvaries.quot We typically denote variables using a lower-case or uppercase letter of the Latin alphabet, such as a a a, b b b, X X X, or Y Y Y

Discrete Variables. Continuous Variable. Nature of Values. They can take only specific or discrete values. They can take any value within a specific range. Measurement Scale. Discrete variables are typically measured on a nominal or ordinal scale. Continuous variables are typically measured on an interval or ratio scale. Representation

Discrete random variables have two classes finite and countably infinite. A discrete random variable is finite if its list of possible values has a fixed finite number of elements in it for example, the number of smoking ban supporters in a random sample of 100 voters has to be between 0 and 100. One very common finite random variable is

Expectation of Random Variables Continuous! X EX xquotfxdx The expected or mean value of a continuous rv X with pdf fx is Discrete Let X be a discrete rv that takes on values in the set D and has a pmf fx. Then the expected or mean value of X is! X EX xquotfx xD

Discrete Probability Distributions. If a random variable is a discrete variable, its probability distribution is called a discrete probability distribution.. An example will make this clear. Suppose you flip a coin two times. This simple statistical experiment can have four possible outcomes HH, HT, TH, and TT. Now, let the random variable X represent the number of Heads that result from this

In probability theory and statistics, the probability distribution of a mixed random variable consists of both discrete and continuous components. A mixed random variable does not have a cumulative distribution function that is discrete or everywhere-continuous. An example of a mixed type random variable is the probability of wait time in a queue.

For example, continuous random variables include the following Height and weight. Time and duration. Temperatures. Analysts denote a continuous random variable as X and its possible values as x, just like the discrete version. However, unlike discrete random variables, the chances of X taking on a specific value for continuous data is zero.

Treating discrete variables as continuous variables. You cannot count someone's exact age because counting in the smallest unit of time a zeptosecond would take forever. therefore, it is a continuous variable. But we sometimes turn age into discrete variables such as age in years so that we can count it.

Here the random variable quotXquot takes 11 values only. Because quotxquot takes only a finite or countable values, 'x' is called as discrete random variable. Continuous Random Variable Already we know the fact that minimum life time of a human being is 0 years and maximum is 100 years approximately Interval for life span of a human being is 0 yrs