1 / 44

Probability Distributions and Statistics

8. Probability Distributions and Statistics. Distributions of Random Variables Expected Value Variance and Standard Deviation Binomial Distribution Normal Distribution Applications of the Normal Distribution. Random Variable.

kura
Download Presentation

Probability Distributions and Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 8 Probability Distributions and Statistics • Distributions of Random Variables • Expected Value • Variance and Standard Deviation • Binomial Distribution • Normal Distribution • Applications of the Normal Distribution

  2. Random Variable A random variable is a rule that assigns a number to each outcome of a chance experiment. • Finite discrete – variable can assume only finitely many values. • Infinite discrete – variable can assume infinitely many values that may be arranged in a sequence. • Continuous – variable can assume values that make up an interval of real numbers.

  3. Probability Distribution for the Random Variable X A probability distribution for a random variable X: Find 0.65 0.67

  4. Ex. Students from a small college were asked how many charge cards they carry. X is the random variable representing the number of cards and the results are below. Probability Distribution

  5. Histograms A way to represent a probability distribution of a random variable graphically. Credit card results:

  6. Mean The average (mean) of the n numbers Median The median is the middle value in a set of data that is arranged in increasing or decreasing order. For an even number of data points the median is the average of the middle two. Mode The mode is the number that occurs most frequently in a set of data.

  7. Ex. The quiz scores for a particular student are given below: 22, 25, 20, 18, 12, 20, 24, 20, 20, 25, 24, 25, 18 Find the mean, median and mode. Mean: Median: Middle number = 20 12, 18, 18, 20, 20, 20, 20, 22, 24, 24, 25, 25, 25 Mode (most frequent): 20 (occurs 4 times)

  8. Expected Value of a Random Variable X Let X denote a random variable that assumes the values x1, x2, …,xn with associated probabilities p1, p2, …, pn, respectively. Then the expected value of X, E(X), is given by

  9. Ex. Use the data below to find out the expected number of credit cards that a student will possess. x = # credit cards =1.97 About 2 credit cards

  10. Ex. Jackson and Max are playing a dice game where a single die is rolled. Jackson pays Max $2 for rolling a 1, 2, 3, or 4 and Max pays Jackson $D for a 5 or 6. Determine the value of D if the game is to be fair. We want the expected value of the game to be zero to be fair: Jackson loss D should be $4

  11. Odds 賠率 If P(E) is the probability of an event E occurring, then 1. The odds in favor of E occurring are given by the ratio E occurs E doesn’t occur 2. The odds against E occurring are given by the ratio E doesn’t occur E occurs

  12. Ex. If the news has just announced that the probability of rain is 0.65 (65%), find a. the odds in favor of rain b. The odds against rain

  13. Probability of an Event (Given Odds) If the odds in favor of an event E occurring are a to b, then the probability of E occurring is Ex. The odds that the horse Gluebound will win a particular race are 2 to 16. Find the probability that Gluebound wins the race.

  14. Variance Variance is a measure of the spread of the data. The larger the variance, the larger the spread. Suppose a random variable has the probability distribution and expected value The variance of a random variable X is defined by:

  15. Standard Deviation Standard deviation is a measure of the spread of the data using the same units as the data. The standard deviation of a random variable X is defined by: Where each xidenotes the value assumed by the random variable X and pi is the probability associated with xi.

  16. Ex. The quiz scores for a particular student are given below: 22, 25, 20, 18, 12, 20, 24, 20, 20, 25, 24, 25, 18 Find the variance and standard deviation.

  17. Chebychev’s Inequality Let X be a random variable, where

  18. Ex. A probability distribution has a mean of 40 and a standard deviation of 12. Use Chebychev’s inequality to estimate the probability that an outcome of the experiment lies between 22 and 58. Notice that So at least 84%

  19. Binomial (Bernoulli) Trials • A binomial experiment has the properties: • The number of trials in the experiment is fixed. • The only outcomes are “success” and “failure.” • The probability of success in each trial is the same. • The trials are independent of each other.

  20. Probabilities in Bernoulli Trials In a binomial experiment in which the probability of success in any trial is p, the probability of exactly x successes in n independent trials is given by

  21. Ex. A card is drawn from a standard 52-card deck. If drawing a club is considered a success, find the probability of a. exactly one success in 4 draws (with replacement). b. no successes in 5 draws (with replacement).

  22. Mean, Variance, and Standard Deviation of a Random Variable X If X is a binomial random variable associated with a binomial experiment consisting of n trials with probability of success p and probability of failure q, then the mean, variance, and standard deviation of X are

  23. Ex. 5 cards are drawn, with replacement, from a standard 52-card deck. If drawing a club is considered a success, find the mean, variance, and standard deviation of X (where X is the number of successes).

  24. Ex. If the probability of a student successfully passing this course (C or better) is 0.82, find the probability that given 8 students a. all 8 pass. b. none pass. c. at least 6 pass. so 6, 7, and 8 successes = 0.8392

  25. Probability Density Function A probability density function, f, defines a continuous probability distribution and coincides with the interval of values taken on by the random variable associated with an experiment. • f (x) is nonnegative for all values of x. • The area of the region between the graph of f and the x – axis is equal to 1. Area = 1

  26. Probability Density Function P(a < X < b) is given by the area of the shaded region. a b

  27. Normal Distributions Normal distributions are a special class of continuous probability density functions. Many phenomena have probability density functions that are normal. The graph of this distribution is called a normal curve. The probability density function associated with the normal curve:

  28. Normal Curve Properties • The peak is at • There is symmetry with respect to the line • The curve lies above and approaches the x–axis. • The area under the curve is 1. • 68.27% of the area lies within 1 standard deviation of the mean, 95.45% within 2, and 99.73% within 3 (see curve on next slide).

  29. Normal Curve Percentage of area within given standard deviations. 99.73% 95.45% 68.27%

  30. Normal curves with the same standard deviation but different means Normal curves with the same mean but different standard deviations.

  31. Standard Normal Distribution Denoted by the variable Z, with Ex. Let Z be the standard normal variable. Find (from table) a. P(Z < 0.85) This is the area to the left of 0.85 0.8023 b. P(Z > 1.32) Use the fact that this area is equivalent to finding P(Z < –1.32) 0.0934

  32. c. P(–2.1 < Z < 1.78) Find the area to the left of 1.78 then subtract the area to the left of –2.1. P(Z < 1.78) – P(Z < –2.1) 0.9625 – 0.0179 = 0.9446

  33. Ex. Let Z be the standard normal variable. Find z if a. P(Z < z) = 0.9278. Look at the table and find an entry = 0.9278 then read back to find z = 1.46. b. P(–z < Z < z) = 0.8132 P(z < Z < –z ) = 2P(0 < Z < z) = 2[P(Z < z ) – ½] = 2P(Z < z) – 1 = 0.8132 P(Z < z) = 0.9066 z = 1.32

  34. Transforming Other Normal Distributions into a Standard Normal Distribution Given X, a normal random variable distribution with We can transform X to Z using:

  35. Ex. Let X be a normal random variable with Find a. P(X < 65) b. P(X > 60) a. P(X < 65) Convert to standard normal = 0.2266

  36. b. P(X > 60) Convert to standard normal = 0.8413

  37. Ex. A particular rash has shown up at an elementary school. It has been determined that the length of time that the rash will last is normally distributed with a. Find the probability that for a student selected at random, the rash will last for less than 3 days. b. Find the probability that for a student selected at random, the rash will last for between 3.75 and 9 days.

  38. a. Find the probability that for a student selected at random, the rash will last for less than 3 days. = 0.0228 b. Find the probability that for a student selected at random, the rash will last for between 3.75 and 9 days. = 0.9772 – 0.0668 = 0.9104

  39. Approximating Binomial Distributions Suppose we are given a binomial distribution associated with a binomial experiment involving n trials, each with probability of success p and failure q. If n is large and p is not close to 1 or 0, the binomial distribution may be approximated by a normal distribution with:

  40. Ex 1. PAR Bearings manufactures ball bearings packaged in lots of 100 each. The company’s quality-control department has determined that 2% of the ball bearings manufactured do not meet the specifications imposed by a buyer. Find the average number of ball bearings per package that fail to meet with the specification imposed by the buyer. The experiment under consideration is binomial. The average number of ball bearings per package that fail to meet with the specifications is therefore given by the expected value of the associated binomial random variable.

  41. Ex 2. At a particular small college the pass rate of Intermediate Algebra is 72%. If 500 students enroll in a semester determine the probability that a. at most 375 students pass. continuous variable Y Convert to Z = 0.9394

  42. b. between 355 and 390, inclusive, of the students pass. = 0.9989 – 0.2912 = 0.7077 or 71%

  43. Assignment for Chapter 8 • § 8.1 1-5,14,18,22 • § 8.2 1-5,8,13 • § 8.3 1-6,16,23,28,36 • § 8.4 1-6, 18, 20,33,40,46 • § 8.5 1-13, 20 • § 8.6 1-5,13,18,21,24

More Related