Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
First i will explain the binomial expansion
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.
If the process can be assumed to follow a Gaussian distribution then 99.7% of the outputs of the process will lie between those two limits. That may be of benefit in quality control if it is a production process.
A simple continuous distribution can take any value between two other values whereas a discrete distribution cannot.
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
Farag Abdel-Salam Attia has written: 'On the distribution function of the interval between zero-crossings of a stationary Gaussian process' -- subject(s): Distribution (Probability theory), Gaussian processes
White noise is a type of signal that has a flat power spectral density across all frequencies, meaning that all frequencies have equal power. Gaussian noise refers to noise with a normal distribution in the time domain. While white noise has uniform power across all frequencies, Gaussian noise has a distribution of values that follows the Gaussian (bell-shaped) curve.
What is the relationship between physical geography and population.
Volcanoes
A Gaussian noise is a type of statistical noise in which the amplitude of the noise follows that of a Gaussian distribustion whereas additive white Gaussian noise is a linear combination of a Gaussian noise and a white noise (white noise has a flat or constant power spectral density).
With a 10 point grading scale the results (of a test etc.) are given a value between 0 and 9 or 1 and 10. If the grading is "on a curve" than the distribution of the various grades is spread on a Gaussian normal distribution.
When studying the behaviour of the sum of independent, identically distributed (iid) Gaussian (normal) variables. The F-distribution is used mainly in the analysis of variance where the errors - between the observed value and those predicted by a model - are assumed to be iid Normal.
They are the same. The full name is the Probability Distribution Function (pdf).
If the distribution is discrete you need to add together the probabilities of all the values between the two given ones, whereas if the distribution is continuous you will need to integrate the probability distribution function (pdf) between those limits. The above process may require you to use numerical methods if the distribution is not readily integrable. For example, the Gaussian (Normal) distribution is one of the most common continuous pdfs, but it is not analytically integrable. You will need to work with tables that have been computed using numerical methods.
First i will explain the binomial expansion
The question does not specify what z is so this answer could be completely wrong. However, if the question is in the context of the standard Gaussian distribution, the answer is 0.493613 (to 6 dp)