Chapter 7 Continuous Probability Distributions
In this chapter we will study probability distributions that arise from continuous random variables, which are outcomes that take on any numerical value in an interval, including numbers with decimal points. Recall that probabilities for specific values of a discrete random variable were easy to calculate and the graph of the discrete probability distribution looks like a histogram with a countable number of bars. By contrast, only the probability of a range of values (not a specific value) can be calculated for a continuous random variable. Why? Since there is an infinite number of possible values for a continuous random variable, the probability of one specific value is theoretically equal to zero! We’re going to study three specific continuous probability distributions and identify the types of data where they are useful.
- Normal Distribution:
Figure 7.0.1. Normal Distribution (Made in GeoGebra by the GeoGebra Materials Team)Link to GeoGebra:https://www.geogebra.org/m/W9Nz53Ct - Exponential Distribution:
Figure 7.0.2. Exponential Distribution (Made in GeoGebra by DavidK)Link to GeoGebra:https://www.geogebra.org/m/gjd2dmzw - Uniform Distribution:
Figure 7.0.3. Uniform Distribution (Made in GeoGebra by David Ramsay)Link to GeoGebra:https://www.geogebra.org/m/v2EnMNF9
