Best Answer

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.

Q: What does the normal probability density function describe?

Write your answer...

Submit

Still have questions?

Continue Learning about Statistics

The probability density function of a random variable can be either chosen from a group of widely used probability density functions (e.g.: normal, uniform, exponential), based on theoretical arguments, or estimated from the data (if you are observing data generated by a specific density function). More material on density functions can be found by following the links below.

Please see the link under "legitimate probability density function".

The Normal curve is a graph of the probability density function of the standard normal distribution and, as is the case with any continuous random variable (RV), the probability that the RV takes a value in a given range is given by the integral of the function between the two limits. In other words, it is the area under the curve between those two values.

The probability density of the standardized normal distribution is described in the related link. It is the same as a normal distribution, but substituted into the equation is mean = 0 and sigma = 1 which simplifies the formula.

Yes.

Related questions

Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.

The probability density function of a random variable can be either chosen from a group of widely used probability density functions (e.g.: normal, uniform, exponential), based on theoretical arguments, or estimated from the data (if you are observing data generated by a specific density function). More material on density functions can be found by following the links below.

Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.

Please see the link under "legitimate probability density function".

Pi is relevant to statistics students as it (for example) appears in the well known normal distribution probability density function.

The Normal curve is a graph of the probability density function of the standard normal distribution and, as is the case with any continuous random variable (RV), the probability that the RV takes a value in a given range is given by the integral of the function between the two limits. In other words, it is the area under the curve between those two values.

The probability density of the standardized normal distribution is described in the related link. It is the same as a normal distribution, but substituted into the equation is mean = 0 and sigma = 1 which simplifies the formula.

I am not quite sure what you are asking. If this answer is not complete, please be more specific. 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.

Most random variables are found to follow the probability distribution function All this means is that most things which can be measured quantitatively, like a population's height, the accuracy of a machine, effectiveness of a drug on fighting bacteria, etc. will occur with a probability that can be calculated according to this equation. Since most things follow this equation, this equation is considered to be the "normal" probability density. "Normal" events follow a "normal" probability distribution.

Yes.

Describe the normal function of the hematic system

(10, 15, 15, 15, 20) The answer above displays a sample in which the sample mean, sample median and sample mode assume the same value. If you were asking about populations, then the population mean, population median and population mode are the same whenever the probability density function for the population is symmetric. For example, the normal probability density function is symmetric, the t and uniform density functions are symmetric. Many are.