# MATH 10: Elementary Statistics and Probability Chapter 4: Discrete Random Variables

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1 MATH 10: Elementary Statistics and Probability Chapter 4: Discrete Random Variables Tony Pourmohamad Department of Mathematics De Anza College Spring 2015

2 Objectives By the end of this set of slides, you should be able to: 1 Recognize and understand discrete probability distribution functions 2 Calculate and interpret expected values 3 Recognize the binomial probability distribution and apply it appropriately 2 / 32

3 Random Variables Random Variable: A random variable describes the outcomes of a statistical experiment Random variables can be of two types: Discrete random variables: can take only a finite or countable number of outcomes. Example: number of patients with a particular disease Continuous random variables: take values within a specified interval or continuum. Example: height or weight The values of a random variable can vary with each repetition of an experiment We will use capitol letters to denote random variables and lower case letter to denote the value of a random variable If X is a random variable, then X is written in words, and x is given a number 3 / 32

4 Example #1 Let X = the number of heads you get when you toss three fair coins The sample space S = {HHH, THH, HTH, HHT, TTH, HTT, THT, TTT} Then, x = 0, 1, 2, or 3. Why? X is in words and x is a number The x values are countable (discrete) outcomes Because you can count the possible values that X can take on and the outcomes are random (the x values 0,1,2,3), X is a discrete random variable 4 / 32

5 Probability Distribution Function A discrete probability distribution function has two characteristics: 1 Each probability is between zero and one, inclusive 2 The sum of the probabilities is one The probability distribution function of a discrete random variable specifies all the possible outcomes of the random variable along with the probability that each will occur Example: Suppose Nancy has classes three days a week. She attends classes three days a week 80% of the time, two days 15% of the time, one day 4% of the time, and no days 1% of the time. X = the number of days Nancy attends classes x could be 0,1,2,or 3 x P(X=x) / 32

6 Example #2 Students who live in the dormitories at a certain four year college must buy a meal plan. They must select from four meal plans: 10 meals, 14 meals, 18 meals, or 21 meals per week. The Food and Housing Office has determined that the 15% of students purchase 10 meal plan, 45% of students purchase the 14 meal plan, 30% of students purchase the 18 meal plan and 10% of students purchase the 21 meal plan. What is the random variable X? Notation: In general, P(X) = probability value In our example, P(X = 10) is the probability that a student purchases a meal plan with 10 meals per week P(X > 14) is the probability that a student purchases a meal plan with more than 14 meals per week 6 / 32

7 Example #2 Continued Make a table that shows the probability distribution x = the number of meals P(x) What is the probability that a student purchases more than 14 meals? i.e., P(X > 14) =??? What is the probability that a student doesn t purchase 21 meals?, i.e., P(X < 21) =??? 7 / 32

8 Mean or Expected Value The expected value is often referred to as the "long-term" average or mean Over the long term of doing an experiment over and over, you would expect this average Imagine flipping a coin, over and over and over... Karl Pearson (who?) once tossed a fair coin 24,000 times! He recorded the results of each coin toss, obtaining heads 12,012 times 12012/ The mean or expected value of an experiment is denoted by µ 8 / 32

9 Mean or Expected Value To find the expected value or long term average, µ, simply multiply each value of the random variable by its probability and add the products What is the expected value of Example #2? x = the number of meals P(x) x P(x) (0.15) = (0.45) = (0.30) = (0.10) = 2.1 µ = Expected Value = = 15.3 So in general, µ = x P(x) 9 / 32

10 Example #3 Let X = the number of heads you get when you toss four fair coins The sample space S = {HHHH, HTHH, HHTH, HHHT, HTTH, HHTT, HTHT, HTTT, THHH, TTHH, THTH, THHT, TTTH, THTT, TTHT, TTTT} Then, x = 0, 1, 2, 3 or 4. Why? What is the expected value of X? x = the number of heads P(x) x P(x) 0 1/16 0 (1/16) = 0 1 4/16 1 (4/16) = /16 2 (6/16) = /16 3 (4/16) = /16 4 (1/16) = / 32

11 Example #3 Continued What is the expected value of X? x = the number of heads P(x) x P(x) 0 1/16 0 (1/16) = 0 1 4/16 1 (4/16) = /16 2 (6/16) = /16 3 (4/16) = /16 4 (1/16) = 0.25 The expected value of X is µ = x P(x) = = 2 Is this answer intuitive? 11 / 32

12 Law of Large Numbers The Law of Large Numbers describes the result of performing the same experiment a large number of times According to the Law of Large Numbers, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed Where does the Law of Large Numbers come up in our every day lives? Counting the number of heads in n tosses of a fair coin Playing a casino game, like roulette, in Las Vegas Games of chance involving rolling a die Playing the lottery Other examples? 12 / 32

13 Law of Large Numbers Imagine tossing a fair coin for a very long time... Number Number Expected Number Chance Percentage Tosses Heads Heads Error Heads % % % % % As I toss the coin more and more the percentage of heads approaches This is the Law of Large Numbers at work! 13 / 32

14 Example #4 Imagine you play a game where you flip a fair coin n times You win \$100 if you obtain more than 75% heads Question: Would you want to throw the coin 10 times or 1000 times? 14 / 32

15 Standard Deviation We can calculate the standard deviation of a random variable X as the following: σ = (x µ)2 P(x) Let X = the number of heads you get when you toss four fair coins x = the # of heads P(x) x P(x) (x µ) 2 P(x) 0 1/16 0 (1/16) = 0 (0 2) 2 (1/16) = /16 1 (4/16) = 0.25 (1 2) 2 (4/16) = /16 2 (6/16) = 0.75 (2 2) 2 (6/16) = 0 3 4/16 3 (4/16) = 0.75 (3 2) 2 (4/16) = /16 4 (1/16) = 0.25 (4 2) 2 (1/16) = 0.25 The standard deviation of X is σ = (x µ)2 P(x) = = 1 15 / 32

16 Example #5 At the county fair, a booth has a coin flipping game You pay \$2.50 to flip two fair coins If the result contains one or two heads, you win \$3 If the result is two tails then there is no prize Question: Write the probability distribution function for the amount won or lost in one game What is the random variable X? The sample space S = {HH, HT, TH, TT} x = amount of money won P(x) \$0.50 3/4 -\$2.50 1/4 16 / 32

17 Example #5 Continued Question: Find the expected value for this game (Expected NET GAIN OR LOSS) x = amount of money won P(x) x P(x) \$0.50 3/4 0.5 (3/4) = \$2.50 1/4 2.5 (1/4) = µ = ( ) ( ) 3 1 x P(x) = ( 2.50) = So you should expect to lose, on average, 25 cents per game (\$0.25) after playing the game over and over again Question: Find the expected total net gain or loss if you play this game 100 times 100 µ = 100 ( \$0.25) = \$25 17 / 32

18 Binomial Distribution There are four characteristics of a binomial experiment 1 There are a fixed number of trials n 2 Each trial has two possible outcomes that we classify as "success" or "failure." The two possible outcomes will be mutually exclusive. 3 The outcomes of the n trials are independent. The outcome of one trial does not influence the outcome of any other trial 4 The probability of success p is constant for each trial In a binomial experiment, the random variable X is the number of successes during the n trials Examples of a binomial experiment? Can a binomial experiment be done without replacement? Notation: X B(n, p) 18 / 32

19 Binomial Distribution Let X be a binomial random variable We say that X B(n, p) The binomial probability distribution is P(X = x) = n! (n x)!x! px (1 p) n x n is the number of trials x is the number of successes in n trials p is the probability of success (1 p) is the probability of failure Recall:! is factorial notation Example: 5! = = / 32

20 Example #6 Let X = the number of heads you get when you toss four fair coins The sample space S = {HHHH, HTHH, HHTH, HHHT, HTTH, HHTT, HTHT, HTTT, THHH, TTHH, THTH, THHT, TTTH, THTT, TTHT, TTTT} What is the probability of getting 2 heads? P(X = 2) = 6/16 = x = the number of heads P(x) 0 1/16 1 4/16 2 6/16 3 4/16 4 1/16 Question: Is there another way to solve this? 20 / 32

21 Example #6 Continued Is X a binomial random variable? n = 4 so fixed number of trials There are only two mutually exclusive outcomes: Heads or Tail We define a "success" as getting a Head and a "failure" as getting a Tail The outcomes of trials are independent The probability of success p = 0.5 for each trial Yes! X is a binomial random variable What is the probability of getting 2 heads? P(X = 2) = 4! (4 2)!2! 0.52 (1 0.5) 4 2 = (0.25) 4 = / 32

22 Example #7 So why is the binomial distribution useful? Look below! Let X = the number of heads you get when you toss 10 fair coins The sample space S = {HHHHHHHHHH, HTHHHHHHHH,...} Turns out there are 1024 ways to flip 10 coins Is the sample space easy to write down? NO! So what is the probability I get 8 heads? P(X = 8) = 10! (10 8)!8! 0.58 (1 0.5) 10 8 = (0.25) = Fast and easy to calculate using binomial distribution 22 / 32

23 Number of Combinations So what does the value of n! (n x)!x! represent? It is the number of ways to get a success out of the total number of trials For example, think of flipping a coin 4 times and you want to get heads twice The sample space is S = {HHHH, HTHH, HHTH, HHHT, HTTH, HHTT, HTHT, HTTT, THHH, TTHH, THTH, THHT, TTTH, THTT, TTHT, TTTT} So there is 6 ways to get two heads 23 / 32

24 Number of Combinations Continued For example, think of flipping a coin 4 times and you want to get heads twice So, n = 4 and x = 2 So the number of combinations is n! = (n x)!x! 4! = (4 2)!2! = 24 4 = 6 Notation: ( ) n = x n! (n x)!x! 24 / 32

25 Expected Value (Mean) and Standard Deviation Let X B(n, p) then: The expected value of X is The standard deviation of X is µ = np σ = np(1 p) These shortcut formulas for µ and σ give the same results as the definitions µ = Σx P(x) and σ = (x µ)2 P(x) but with a lot less work! 25 / 32

26 Example #8 Recall the following probability distribution table: Let X = the number of heads you get when you toss four fair coins x = the # of heads P(x) x P(x) (x µ) 2 P(x) 0 1/16 0 (1/16) = 0 (0 2) 2 (1/16) = /16 1 (4/16) = 0.25 (1 2) 2 (4/16) = /16 2 (6/16) = 0.75 (2 2) 2 (6/16) = 0 3 4/16 3 (4/16) = 0.75 (3 2) 2 (4/16) = /16 4 (1/16) = 0.25 (4 2) 2 (1/16) = 0.25 The expected value of X is µ = x P(x) = = 2 The standard deviation of X is σ = (x µ)2 P(x) = = 1 26 / 32

27 Example #8 Continued Let X = the number of heads you get when you toss four fair coins Is X a binomial random variable? So, n = 4 and p = 0.5 The expected value of X is The standard deviation of X is µ = np = 4 (0.5) = 2 σ = np(1 p) = 4 (0.5) (0.5) = 1 Much faster to use the formulas for expected value and standard deviation of a binomial random variable! 27 / 32

28 Example #9 A college claims that 70% of students receive financial aid. Suppose that 10 students at the college are randomly selected. We are interested in the number of students in the sample who receive financial aid. Is this a binomial experiment? What is the random variable X? What is n? What is p? 28 / 32

29 Example #9 Continued A college claims that 70% of students receive financial aid. Suppose that 10 students at the college are randomly selected. We are interested in the number of students in the sample who receive financial aid. X is the number of students who received financial aid The number of trials is n = 10 The probability of receiving financial aid is p = 0.7 Question: What is the probability that two students received financial aid? P(X = 2) = 10! (10 2)!2! (0.72 ) (1 0.7) 8 = / 32

30 Example #9 Continued Question: What is the probability that less than two students received financial aid? P(X < 2) = P(X = 0) + P(X = 1) = 10! (10 0)!0! (0.70 )(1 0.7) 10 10! + (10 1)!1! (0.71 )(1 0.7) 9 = Question: What is the probability that less than or equal to 9 students received financial aid? P(X 9) = P(X = 0) + P(X = 1) P(X = 8) + P(X = 9) or P(X 9) = 1 P(X = 10) 30 / 32

31 Example #9 Continued What is the expected number of students who should receive financial aid? The expected number of students who should receive financial aid is µ = np = 10 (0.7) = 7 What is the standard deviation? σ = np(1 p) = 10(0.7)(0.3) = 2.1 = / 32

32 Other Discrete Probability Distributions Some other well known discrete probability distributions: The Poisson distribution The geometric distribution The hypergeometric distribution The negative-binomial distribution And the list goes on / 32

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