Finite Mathematics Lesson 9
Chapters 6.5  6.7
Some probabilities are not easy to set up. Warning ! Some of this material is hard. This lesson explores conditional probabilities where we want to find the probability of a event given some other event has already occurred. We also talk independence of events and what that means. Finally, Bayes' formula is introduced but before you panic, I won't make you memorize it. I will show you how to use it. This chapter really makes us think... Sometimes it hurts me !
Course Notes
Section 6.5 Conditional Probability and Independence
Conditional Probability
A coin is tossed 3 times. The sample space is
KEY : H T H = Heads (1st toss) Tails(2nd toss) Heads(3rd toss)
H H H  H H T  H T H  H T T 
T T T  T T H  T H T  T H H 
What is the probability on tossing exactly 2 Heads if we know the first toss is a Head?
What is the probability on tossing 2 Heads and 1Tail if we know the first toss is a Tail?
A New Formula
As you can imagine, when the sample space gets large it would be impossible to list all
the outcomes.
We need a new formula.
This is read as "The probability of Event F given Event F"
It is sometimes useful to write as (Product Rule)
Let's look at this problem again.
What is the probability on tossing exactly 2 Heads if we know the first toss is a Head?
Let E = tossing exactly 2 Heads
Let F = the first toss is a Head
= tossing exactly 2
Heads AND the first toss is a Head
Pr(F) = 4/8 = 0.5
Let's look at this problem again.
What is the probability on tossing 2 Heads and 1Tail
if we know the first toss is a Tail? { T H H }
Let E = tossing 2 Heads
and 1Tail
Let F = the first toss is a Tail
= tossing 2
Heads and 1Tail
AND the first toss is a Tail
Pr(F) = 4/8 = 0.5
Example #12 page 291
Let E and F be events with Pr(E) = 0.3, Pr(F) =
0.6, and
Find the probabilities:
From before we know 

Using the fact that Think about it (page 278, practice problem 2, example 2) So rearranging the formula
gives 

Independence
Let E and F be events. We say that E and
F are independent provided 
What does this mean? If the outcome of an event has no affect on the outcome
of the other event , the events are called independent.
Example Toss 2 fair coins.
Example Roll 2 dice.
These events are independent
because coins or dice have no memory.
Every event is a new event.
Counter Example
Suppose we pick 2 balls out of an urn without
replacement. Let's say 5 are red and 7 are blue.
Pr(red) = 5/12
and Pr(blue) = 7/12
What is the probability of picking a red ball then a blue ball?
Pr(red,blue) = 5/12 × 7/11 , notice
it is not 5/12 × 7/12 because there are less balls in the urn on the second pick.
Therefore, these events are not independent.
A final note: If 2 or more events are independent, you can multiply their probabilities without looking at a new sample space or using the formula for conditional probabilities.
Section 6.6 Tree Diagrams
Nothing really new here other than I good way to organize data. Check this out...
Problem #18, page 300 (page 288 in old
edition) A factory has two machines that produces bolts. Machine I produces 60% of the daily output of bolts. , and 3% of its bolts are defected. Machine II produces 40% of the daily output of bolts. , and 2% of its bolts are defected. 
Section 6.7 Bayes' Theorem
Bayes' theorem looks very complicated, but, wait... it is complicated. Let's look
at
Problem #18, page 300 again.
b. A factory has two machines that produces bolts. Machine I produces 60% of the daily output of bolts. , and 3% of its bolts are defected. Machine II produces 40% of the daily output of bolts. , and 2% of its bolts are defected. 
If a bolt is selected at random and found to be defected,
what is the probability that it was produce by machine I?
Let B_{1} = produced by Machine I  Let B_{2} = produced by Machine II 
Pr(B_{1}) = 0.6  Pr(B_{2}) = 0.4 
Pr( D  B_{1}) = 0.03 probability bolt is defective given it was produce by machine I 
Pr( D  B_{2}) = 0.02 probability bolt is defective given it was produce by machine II 
We want to find the probability that bolt selected at
random was produced by Machine I. That is Pr( B_{1}  D). This is not the same as Pr( D  B_{1}) where the machine was picked before the bolt was selected. Note :I used Pr( I  D) in the problem above. 

By Bayes' theorem  Notice the pattern! 
Recall from above, that this is the same solution we got with the tree. (part b) 
Example page 305, problem 2. Look at Table #3
An electronic device has six different types of transistors. For each type of transistors, table 3 gives the proportion of the total numbers of transistors of that type and the failure rate(probability of failing within one year). If a transistor fails, what is the probability that it is of type 1. 
We want to find Pr( Type 1  transistor fails) = Pr(Type 1  F) where F = fails.
or more simply put...
Pr(Type 1 F) = 0.0698
A final note on Bayes' Theorem
Please notify me of any errors on this page joe@joemath.com