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Chapter. 6. Discrete Probability Distributions. Section. 6.1. Discrete Random Variables. Objectives Distinguish between discrete and continuous random variables Identify discrete probability distributions Construct probability histograms

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  1. Chapter 6 Discrete Probability Distributions

  2. Section 6.1 Discrete Random Variables

  3. 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 • Compute the standard deviation of a discrete random variable

  4. Objective 1 • Distinguish between Discrete and Continuous Random Variables

  5. A random variable is 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.

  6. A 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.

  7. A continuous random variable has infinitely many values. The values of a continuous random variable can be plotted on a line in an uninterrupted fashion.

  8. EXAMPLE Distinguishing Between Discrete and Continuous Random Variables • Determine whether the following random variables are discrete or continuous. State possible values for the random variable. • The number of light bulbs that burn out in a room of 10 light bulbs in the next year. • (b) The number of leaves on a randomly selected oak tree. • (c) The length of time between calls to 911. Discrete; x = 0, 1, 2, …, 10 Discrete; x = 0, 1, 2, … Continuous; t > 0

  9. Objective 2 • Identify Discrete Probability Distributions

  10. A probability distribution provides the possible values of the random variable X and their corresponding probabilities. A probability distribution can be in the form of a table, graph or mathematical formula.

  11. EXAMPLE A Discrete Probability Distribution The table to the right shows the probability distribution for the random variable X, where X represents the number of movies streamed on Netflix each month.

  12. Rules for a Discrete Probability Distribution Let P(x) denote the probability that the random variable X equals x; then • ΣP(x) = 1 • 0 ≤ P(x) ≤ 1

  13. EXAMPLE Identifying Probability Distributions Is the following a probability distribution? No. ΣP(x) = 0.97

  14. EXAMPLE Identifying Probability Distributions Is the following a probability distribution? No. P(x = 5) = –0.01

  15. EXAMPLE Identifying Probability Distributions Is the following a probability distribution? Yes. ΣP(x) = 1

  16. Objective 3 • Construct Probability Histograms

  17. A probability histogram is a histogram in which the horizontal axis corresponds to the value of the random variable and the vertical axis represents the probability of that value of the random variable.

  18. EXAMPLE Drawing a Probability Histogram Draw a probability histogram of the probability distribution to the right, which represents the number of movies streamed on Netflix each month.

  19. Objective 4 • Compute and Interpret the Mean of a Discrete Random Variable

  20. The Mean of a Discrete Random Variable The mean of a discrete random variable is given by the formula where x is the value of the random variable and P(x) is the probability of observing the value x.

  21. EXAMPLE Computing the Mean of a Discrete Random Variable Compute the mean of the probability distribution to the right, which represents the number of DVDs a person rents from a video store during a single visit.

  22. Interpretation of the Mean of a Discrete Random Variable Suppose an experiment is repeated n independent times and the value of the random variable X is recorded. As the number of repetitions of the experiment increases, the mean value of the n trials will approach μX, the mean of the random variable X. In other words, let x1 be the value of the random variable X after the first experiment, x2 be the value of the random variable X after the second experiment, and so on. Then The difference between and μX gets closer to 0 as n increases.

  23. The following data represent the number of DVDs rented by 100 randomly selected customers in a single visit. Compute the mean number of DVDs rented.

  24. As the number of trials of the experiment increases, the mean number of rentals approaches the mean of the probability distribution.

  25. Objective 5 • Compute and Interpret the Mean of a Discrete Random Variable

  26. Because the mean of a random variable represents what we would expect to happen in the long run, it is also called the expected value, E(X), of the random variable.

  27. EXAMPLE Computing the Expected Value of a Discrete Random Variable A term life insurance policy will pay a beneficiary a certain sum of money upon the death of the policy holder. These policies have premiums that must be paid annually. Suppose a life insurance company sells a $250,000 one year term life insurance policy to a 49-year-old female for $530. According to the National Vital Statistics Report, Vol. 47, No. 28, the probability the female will survive the year is 0.99791. Compute the expected value of this policy to the insurance company. Survives Does not survive E(X) = 530(0.99791) + (-249,470)(0.00209) = $7.50

  28. Objective 6 • Compute the Standard Deviation of a Discrete Random Variable

  29. Standard Deviation of a Discrete Random Variable The standard deviation of a discrete random variable X is given by where x is the value of the random variable, μXis the mean of the random variable, and P(x) is the probability of observing a value of the random variable.

  30. EXAMPLE Computing the Variance and Standard Deviation of a Discrete Random Variable Compute the variance and standard deviation of the following probability distribution which represents the number of DVDs a person rents from a video store during a single visit. Remember, the mean that we found was 1.49.

  31. EXAMPLE Computing the Variance and Standard Deviation of a Discrete Random Variable

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