QMS 102 Lecture Notes - Lecture 8: Probability Distribution, Standard Deviation, Box Plot

• School: Ryerson University
• Department: Quantitative Methods
• Course: QMS 102
• Professor: Gunawardena Egodawatte

Document Summary

In this lesson, you will learn about continuous probability distributions and three examples of them are the normal, uniform, and exponential distributions. We learn only the normal distribution in this course. This distribution has the following properties: bell-shaped and symmetrical, mean, median, and mode are equal. 1: interquartile range spreads over two-third of the standard deviation above the mean and two-third of the standard deviation below the mean. Q3: the random variable spreads from negative infinity to positive infinity. The normal distribution is important due to 3 main reasons. It has wide range of applications in the business world. It can be used to approximate binomial and poisson distributions. It is very useful in statistical inference because of its relationship to central limit theorem. Since the normal distribution is a continuous probability distribution, we can calculate the probability of the random variable between two values (in an interval). The probability for a particular value of the random variable is zero.