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- Michigan
- Central Michigan University
- Statistics
- Statistics 282
- Wang
- STA_282_Ch_6_Sec_1_Notes.doc

Timothy B.

Section 6. 1 Probability Rules Objectives- Distinguish between discrete and continuous random variables Identify discrete probability distributions Construct probability histograms Compute and interpret the mean of a discrete random variable Interpret the mean of a discrete random variable as an expected value. Compute the variance and standard deviation of a discrete random variable. Random Variable: A numerical measure of the outcome from a probability experiment, so its value is determined by chance. Random variables are denoted using letters such as X. Discrete Random Variable: Has either a finite or countable number of values. The values of a discrete random variable can be plotted on a number line with space between each point. Continuous Random Variable: Has infinitely many values. The values of a continuous random variable can be plotted on a line in an uninterrupted fashion. Probability Distribution: The probability distribution of a discrete random variable X provides the possible values of the random variable and their corresponding probabilities. A probability distribution can be in the form of a table, graph, or mathematical formula. Rules for Discrete Probability Distribution Let P(x) denote the probability that the random variable X equals x; then Sigma P(x)=1 0

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