- Text before the first section.
+ This chapter establishes the basic terminology of sample spaces, events, probability, conditi8onal probability, and independence.
+ This language lets us model and talk about situations that involve randomness (or lack of knowledge which can sometimes be mathematically modeled in the same way).
Set Theory
+
+ When we perform an experiment, there are many results that we might see.
+ We want to be able to quantify the likelihood of seeing certain results.
+ For this, we need to develop some mathematical terminology.
+
+
@@ -21,7 +28,7 @@
-
+
An experiment consists of rolling a standard 6-sided die.
@@ -44,6 +51,7 @@
\amp (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), \amp (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6)\}
+ Note that, for example, (1, 2) is a different outcome from (2, 1).
@@ -58,6 +66,10 @@
+
+ The subset symbol includes the possibility that A and B are equal sets, i.e., that they contain precisely the same elements.
+
+
@@ -108,8 +120,38 @@
- It's useful sometimes to draw pictures representing sets...
+ It's useful sometimes to draw pictures called Venn diagrams representing sets:
+
+
+
Example Venn Diagram
+
+
+
+ Venn diagram showing sets A, B, C with the region representing (A\cup B\cup C) - (A \cap C) shaded.
+
- Suppose we roll a 6-sided die two times.
+ Suppose we have a 6-sided die that's weighted to roll a 6 half of the time.
+ We roll the die two times.
List the set of all possible results.
[Note: the result (2, 4)---rolling a 2 and then a 4---is different from the result (4, 2)---rolling a 4 and then a 2.]
+
+
+
+
+ \Omega = \{\amp (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6),
+ \amp (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6),
+ \amp (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6),
+ \amp (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6),
+ \amp (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6),
+ \amp (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6)\}
+
+ Note that \Omega simply lists outcomes with no reference to the probabilities.
+ So the answer here is the same as in .
+
+
@@ -200,6 +258,31 @@
What about flipping three times? Four times? If we flip the coin 10 times, how many possible results will there be?
+
+
+
+ For two flips: \Omega = \{ HH, HT, TH, TT \}.
+
+
+
+ For three flips: \Omega = \{ HHH, HHT, HTH, THH, HTT, THT, TTH, TTT \}.
+
+ Each additional flip doubles the number of outcomes.
+ So, with ten flips, we'll have |\Omega| = 2^{10} = 1024.
+
+
@@ -208,6 +291,13 @@
If we roll a 6-sided die ten times, how many possible results will there be?
+
+
+
+ Each additional roll will multiply the number of outcomes by 6.
+ So, with 10 rolls, we'll have |\Omega| = 6^{10}.
+
+
@@ -215,6 +305,10 @@
Definition of Probability
+
+ Now that we have the language to refer to outcomes and events of an experiment, we want to start quantifying how likely those outcomes/events are to occur.
+
+
@@ -316,6 +410,11 @@
+
+ When we talk about a fair coin flip or a fair die roll, the word "fair" is indicating a uniform distribution.
+ However, don't make the mistake of assuming that all distributions are uniform by default.
+
+
@@ -323,7 +422,17 @@
What is the probability that they picked 3?
-
+
+
+
+ Without assuming the distribution is fair (i.e., that each value 1, 2, \dotsc, 10) has probability 1/10 of occurring), we don't have enough information to answer this question.
+
+
+
+ In fact, the situation is even more vague than that: the sample space itself is unclear.
+ Are we only allowed to pick integer values? What about fractions like 7/2? What about irrational numbers like \pi?
+
+
@@ -389,8 +498,13 @@
-
+
+
+
+ \Pr(A) = 0.45, \Pr(B) = 0.4, \Pr(A \cup B) = 0.6, \Pr(A \cap B) = 0.25.
+
+
@@ -409,6 +523,12 @@
Write all outcomes in the sample space \Omega.
+
+
+
+ \Omega = \{HH, HT, TH, TT\}.
+
+
@@ -418,6 +538,39 @@
Make a probability distribution table for \Omega assuming the coin is fair.
+
+
+
+ Probability Distribution for Two Fair Coin Flips
+
+
+
+ x
+ \Pr(x)
+
+
+
+ HH
+ 0.25
+
+
+
+ HT
+ 0.25
+
+
+
+ TH
+ 0.25
+
+
+
+ TT
+ 0.25
+
+
+
+
@@ -427,6 +580,39 @@
Make a probability distribution table assuming the coin comes up heads with probability 0.3.
+
+
+
+ Probability Distribution for Two Fair Coin Flips
+
+
+
+ x
+ \Pr(x)
+
+
+
+ HH
+ 0.09
+
+
+
+ HT
+ 0.21
+
+
+
+ TH
+ 0.21
+
+
+
+ TT
+ 0.49
+
+
+
+
@@ -445,6 +631,19 @@
Write all outcomes in the sample space \Omega.
+
+
+
+
@@ -454,6 +653,13 @@
Make a probability distribution table for \Omega assuming the die is fair.
+
+
+
+ We'll avoid an overly large table and note that, since the die is fair, every outcome is equally likely.
+ Therefore, \Pr(x) = \frac{1}{36} for every x\in \Omega.
+
+
@@ -461,9 +667,27 @@
Let A be the event that the second roll is higher than the first, and let B be the event that the first roll is even.
- Find \Pr(A), \Pr(B), and \Pr(A \mid B).
+ Find \Pr(A), \Pr(B), and \Pr(A \cap B).