This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Lecture Unit 4 Section 4.2 Random Variables and Probability Models: Binomial, Geometric and Poisson Distributions Streamline Treatment of Probability ❚ Sample spaces and events are good starting points for probability ❚ Sample spaces and events become quite cumbersome when applied to reallife businessrelated processes ❚ Random variables allow us to apply probability, risk and uncertainty to meaningful businessrelated situations Bring Together Lecture Unit 2, and Section 4.1 ❚ In Lecture Unit 2 we saw that data could be graphically and numerically summarized in terms of midpoints, spreads, outliers, etc. ❚ In Section 4.1 we saw how probabilities could be assigned to outcomes of an experiment. Now we bring them together First: Two Quick Examples 1. Hardee’s vs. The Colonel Hardee’s vs The Colonel ❚ Out of 100 tastetesters, 63 preferred Hardee’s fried chicken, 37 preferred KFC ❚ Evidence that Hardee’s is better? A landslide ? ❚ What if there is no difference in the chicken? (p=1/2, flip a fair coin) ❚ Is 63 heads out of 100 tosses that unusual? Example 2. Mothers Identify Newborns Mothers Identify Newborns ❚ After spending 1 hour with their newborns, blindfolded and nosecovered mothers were asked to choose their child from 3 sleeping babies by feeling the backs of the babies’ hands ❚ 22 of 32 women (69%) selected their own newborn ❚ “far better than 33% one would expect …” ❚ Is it possible the mothers are guessing? ❚ Can we quantify “far better”? Graphically and Numerically Summarize a Random Experiment ❚ Principal vehicle by which we do this: random variables ❚ A random variable assigns a number to each outcome of an experiment Random Variables ❚ Definition: A random variable is a numericalvalued function defined on the outcomes of an experiment S Number line Random variable Examples ❚ S = {HH, TH, HT, TT} the random variable: ❚ x = # of heads in 2 tosses of a coin ❚ Possible values of x = 0, 1, 2 Two Types of Random Variables ❚ Discrete: random variables that have a finite or countably infinite number of possible values ❚ Test: for any given value of the random variable, you can designate the next largest or next smallest value of the random variable Examples: Discrete rv’s ❚ Number of girls in a 5 child family ❚ Number of customers that use an ATM in a 1hour period. ❚ Number of tosses of a fair coin that is required until you get 3 heads in a row (note that this discrete random variable has a countably infinite number of possible values: x=3, 4, 5, 6, 7, . . . ) Two types (cont.) ❚ Continuous: a random variable that can...
View
Full Document
 Spring '08
 reiland
 Business, Probability, Probability distribution, probability density function, economic scenario, HEALTH MARKETING INC ESCALADE INC DBA SYSTEMS INC NEUTROGENA CORP MICROAGE INC CROWN BOOKS CORP AST RESEARCH INC JACO ELECTRONICS INC ADAC LABORATORIES KIRSCHNER MEDICAL CORP

Click to edit the document details