The standard normal distribution is a normal distribution with μ = 0 and σ = 1. The normal distribution is also known as the Gaussian distribution and it denotes the equation or graph which are bell-shaped. Well, you could manually compute it from an integral over the normal distribution formula. Then determine whether the normal distribution can be used to estimate this probability. Scroll down the page for more examples and solutions on using the normal distribution formula. The following diagram shows the formula for Normal Distribution. To recall, a table that assigns a probability to each of the possible outcomes of a random experiment is a probability distribution table. Probability distribution formula mainly refers to two types of probability distribution which are normal probability distribution (or Gaussian distribution) and binomial probability distribution. Probability Density Function The general formula for the probability density function of the normal distribution is $$f(x) = \frac{e^{-(x - \mu)^{2}/(2\sigma^{2}) }} {\sigma\sqrt{2\pi}}$$ where μ is the location parameter and σ is the scale parameter.The case where μ = 0 and σ = 1 is called the standard normal distribution.The equation for the standard normal distribution is We write X - N(μ, σ 2. Normal Probability Distribution Formula. If … Standard Normal Distribution. Formula to Calculate Standard Normal Distribution. An easier option, however, is to look it up in Googlesheets as we'll show later on. The normal distribution was first introduced by the French mathematician Abraham De Moivre in 1733 and was used by him to approach opportunities related to the binom probability distribution if the binom parameter n is large. Standard Normal Distribution is a type of probability distribution that is symmetric about the average or the mean, depicting that the data near the average or the mean are occurring more frequently when compared to the … The normal distribution is described by two parameters: the mean, μ, and the standard deviation, σ. In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional normal distribution to higher dimensions.One definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. Solution for Compute P(X) using the binomial probability formula.