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There are many probability density functions (pdf) of continuous variables, including the Normal, exponential, gamma, beta, log normal and Pareto.
There are many links on the internet. I felt that the related link gives a very "common sense" approach to understanding pdf's and their relationship to probability of events.
As explained in the video, a probability can be read directly from a discrete distribution (called a probability mass function) but in the case of a continuous variable, it is the area under the curve that represents probability.
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For a continuous random variable the probability that the value of x is greater than a given constant is?
The integral of the density function from the given point upwards.
What is the formula for a random variable?
The formula, if any, depends on the probability distribution function for the variable. In the case of a discrete variable, X, this defines the probability that X = x. For a continuous variable, the probability density function is a continuous function, f(x), such that Pr(a < X < b) is the area under the function f, between a and b (or the definite integral or f, with respect to x, between a and b.
Can the Poisson distribution be a continuous random variable or a discrete random variable?
True
Is temperature an example of continuous variable?
Yes. It is a continuous variable. As used in probability theory, it is an example of a continuous random variable.
What is the difference between a discrete and a continuous probability function?
A discrete random variable (RV) can only take a selected number of values whereas a continuous rv can take infinitely many.
Does the probability that a continuous random variable take a specific value depend on the probability density function?
No. The probability that a continuous random variable takes a specific value is always zero.
For a continuous random variable the probability that the value of x is greater than a given constant is?
The integral of the density function from the given point upwards.
How do you get the median of a continuous random variable?
You integrate the probability distribution function to get the cumulative distribution function (cdf). Then find the value of the random variable for which cdf = 0.5.
What is the formula for a random variable?
The formula, if any, depends on the probability distribution function for the variable. In the case of a discrete variable, X, this defines the probability that X = x. For a continuous variable, the probability density function is a continuous function, f(x), such that Pr(a < X < b) is the area under the function f, between a and b (or the definite integral or f, with respect to x, between a and b.
Is the gender of college students a discrete random variable a continuous random variable or not a random variable?
It is a discrete random variable.
Can the Poisson distribution be a continuous random variable or a discrete random variable?
True
Is temperature an example of continuous variable?
Yes. It is a continuous variable. As used in probability theory, it is an example of a continuous random variable.
How many random variables are needed to plot a probability distribution?
A probability density function can be plotted for a single random variable.
Difference between a random variable and a probability distribution is?
A random variable is a variable that can take different values according to a process, at least part of which is random.For a discrete random variable (RV), a probability distribution is a function that assigns, to each value of the RV, the probability that the RV takes that value.The probability of a continuous RV taking any specificvalue is always 0 and the distribution is a density function such that the probability of the RV taking a value between x and y is the area under the distribution function between x and y.
Random variable that can take any numeric value within a range of values?
continuous random variable
What does the normal probability density function describe?
A probability density function (pdf) for a continuous random variable (RV), is a function that describes the probability that the RV random variable will fall within a range of values. The probability of the RV falling between two values is the integral of the relevant PDF. The normal or Gaussian distribution is one of the most common distributions in probability theory. Whatever the underlying distribution of a RV, the average of a set of independent observations for that RV will by approximately Gaussian.
Define continuous random evevt?
Usually we consider a random variable which assigns a value to the outcome of an event. The value assigned to the outcome can be either discrete or continuous. The continuous random variable is a random variable whose domain is defined over a continuous range. Examples: Daily inches of rain, speed of cars on highway, purchases made everyday at grocery stores.
