1/10/2012
Deal?
Golden Road Game
Video
POD #5
1/11/2012
MC
#9
It is Wednesday once again…. Time for our weekly MC practice!
Remember to use your resources – talk it through with your partners, use your notebook, but really try each problem! You don’t get better by just watching others work problems
Stats: Modeling the
World
Chapter 16
Random Variables
Random Variables
A random variable assumes any of several different values as a result of some random event.
Discrete random variables can take one of a finite number of distinct outcomes.
Example: Number of credit hours
Continuous random variables can take any numeric value within a range of values.
Example: Length of your desk
Let’s check…
Discrete or Continuous?
The number of desks in a classroom.
Discrete
The fuel efficiency (mpg) of an automobile.
Continuous
The distance that a person throws a baseball.
Continuous
The number of questions asked during a statistics exam.
Discrete
From Yesterday’s Lab…
The probability model for a random variable consists of:
-the collection of all the possible values and
-the probability that they occur
Mean/Expected Value
Variance & Standard Dev
E ( x ) x P ( x )
Var ( X ) ( x ) P ( x )
2
SD ( X ) variance
Expected Winnings?
A club sells raffle tickets for $5. There are 10 prizes of $25 and 1 grand prize of $100. If 200 tickets are sold…
Create the probability model:
Prize
25
100
Prob
10/200
1/200
0
189/200
Can you figure out how much you can expect to win?
10
1 189
E (x ) 25
100
0
1.75
200
200 200
With what variance?
10
2 1 var( x ) ( 25 1.75)
(100 1.75)
...
200
200
2
78.1875
Example
A couple plans to have children until they get a girl, but they agree that they will not have more than three children even if all are boys. (Assume boys and girls are equally likely.)
a) Make a list of all possible children combinations they could have.
G, BG, BBG, BBB
b) Create a probability model for the number of children they’ll have.
# of kids
Probability
1
.50
2
3
.25
.25
b) How many children can this family expect to have?
E (x ) 1 0.50 2 0.25 3 0.25
1.75
Example
The probability model below describes the number of repair calls that an appliance repair shop may receive during an hour.
Repair calls
0
1
2
3
Probability
0.1
0.3
0.4
0.2
a) How many calls should the shop expect per hour?
E (x ) 0 0.1 1 0.3 2 0.4 3 0.2 1.7
b) What is the variance and standard deviation?
var( x ) (0 1.7) 2 (0.1) ... (3 1.7) 2 (0.2) 0.81
stdev ( x ) 0.81 0.9
POD #6
Deal/No Deal
1/12/2012
Linear Transformation Rules
Flashback!!!
Recall…
- Adding or subtracting a constant changes the MEAN, but NOT the SPREAD (standard deviation or variance)
For random variables, that means:
E(X ± c) = E(X) ± c
Var(X ± c) = Var(X)
Another Flashback!!!
Recall…
- Multiplying by a constant changes BOTH the MEAN
AND the SPREAD (standard deviation or variance)
Note: Since Variance is a squared term, it will change by the constant squared
For random variables, that means:
E(aX) = aE(X)
Var(aX) = a Var(X)
2
POD #7
FRQ
1/13/2012
AP
It’s Friday!!!
You have 15 minutes to answer the AP Free
Response question to the best of your ability.
Remember – NO BLANKS!! ALWAYS TRY!!
Afterwards, you will need yesterday’s worksheet, calculator, paper, and pencil
Combining Random Variables
Combining Random Variables
Means:
When adding two random variables (X and Y), the mean of the total = _________________________
When subtracting two random variables (X and Y), the mean of the differences = ________________
E(X ± Y) = E(X) ± E(Y)
Combining Random Variables
Variances:
When adding two INDEPENDENT random variables (X and Y), the variance of the total = ________________
When subtracting two INDEPENDENT random variables (X and Y), the variance of the difference =
______________
NOTE: