From the course: Probability Foundations for Data Science

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Normal distribution

Normal distribution

From the course: Probability Foundations for Data Science

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Normal distribution

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- [Instructor] Let's expand upon the standard normal distribution by looking at the normal distribution. The normal distribution, like the standard normal distribution, is a continuous probability distribution that helps measure natural phenomena. With its symmetry and bell shaped curve, it is commonly used in a variety of applications and scenarios. In this case though, the expectation is not necessarily equal to zero and the variance is not necessarily equal to one. The normal distribution has two variables. First, it has mu, which represents the expectation, and second, it has sigma squared, which represents the variance. The normal distribution is represented by the following probability density function, where it represents the probability of a random variable taking on a particular value. So you have F of X with mu and sigma squared, and this is equal to one divided by the square root of two multiplied by pi multiplied by sigma squared. And you multiply all that by E to the…

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