Normal Distributions

Calculating Densities

The Problem

Let X be a random variable following a Normal (Gaussian) distribution. All Normal distributions have two parameters: mean and standard deviation (or variance). For this X, let μ = 31 and σ = 4. An example of where such a distribution may arise is the following:

You have a bag of candy made by Statistics, Inc. The weight of the pieces are not all the same, they are a random variable. This variable follows a Normal distribution with average weight 31 grams and standard deviation 4. Define the random variable X as the weight of a randomly selected piece of candy.

For those who like pictures, here is a graphic of the probability density function (pdf). It is not a probability, it is a density. It can be used to determine which values are more likely than others. From the graphic, we can tell that weights are more likely around 31 than around 23 or 37.

…  21 31 41  …

The following is a graphic of the culumative distribution function (CDF). It is a probability. Specifically, it graphs P[X ≤ x] against x. Note that it starts at zero and smoothly climbs to 1.

…  21 31 41  …

Continuing the candy example, let us calculate the probability that the next piece of candy will have a weight 22.692 grams or less; that is, calculate P[X ≤ 22.692]. This is notationally equivalent to calculating F(22.692).

Your Answer

In the box below, please enter the value of F(22.692), i.e. of P[X ≤ 22.692]. We are given that X ~ Normal(μ=31; σ=4). When you have entered your value, click on the “Check your answer!” button. Please round your answer to the ten-thousandths place.

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© Ole J. Forsberg, Ph.D. 2024. All rights reserved.   .