Rules of the game: Syllabus and time table are available at: Read Appendix A Also check these links for some historic background: You will be asked questions in class! We will use the whiteboard and power point whenever it is convenient.

Probability Theory: Probability theory is the branch of mathematics concerned with analysis of random phenomena. (Encyclopedia Britannica) Probability theory: “is the branch of mathematics concerned with” the study and modeling “of random phenomena.” (I added my two cents.) Agreeing with Lindley, this branch allows us to understand and quantify uncertainty. (Dennis V. Lindley Understanding uncertainty.) Examples of uncertain phenomena.

Sample Space and Events An experiment: is any action, process or phenomenon whose outcome is subject to uncertainty (in a very loose sense!) An outcome: is a result of an experiment. Each trial of an experiment results in only one outcome! A trial: is a run of an experiment

Sample Space and Events Examples: 1)Studying the chance of observing a head (H) or a tail (T) when flipping a coin once:

Sample Space and Events Some assumptions to keep in mind: 1.Experiments are assumed to be repeatable under, essentially, the same conditions. (Not always possible and not always necessary.) 2.Which outcome we will attain in a trial is uncertain, is not known before we run the experiment, but, 3.The set of all possible outcomes can be specified before performing the experiment.

Sample Space and Events Examples: 1)Studying the chance of observing a head (H) or a tail (T) when flipping a coin once:

Sample Space and Events Def : A sample space: is the set of all possible outcomes, S, of an experiment. Again, we will observe only one of these in one trial. Def : An event: is a subset of the sample space. An event occurs when one of the outcomes that belong to it occurs. Def : An elementary (simple) event: is a subset of the sample space that has only one outcome.

Sample Space and Events Examples: 1)Studying the chance of observing a head (H) or a tail (T) when flipping a coin once: Experiment: Set of possible outcomes (sample space), S: Flipping a coin {H,T} Goal: Observe whether the coin will be H or T Collection of possible events (Possible subsets of S): {, {H}, {T}, {H,T}=S} : is the empty set. S: the sure event.

Sample Space and Events PopulationSample Probability We are interested in the chance that some event will occur.

Sample Space and Events Before observing an outcome of an experiment (i.e. before any trials) each of the possible events will have some chance of occurring. In probability we quantify this chance and hence speculate about what an event might look like given what we might know about the experiment and the associated population.

Sample Space and Events Examples: 2)Studying the chance of observing a head (H) when flipping a coin once: Experiment: Set of possible outcomes (sample space), S: Flipping a coin {1, 0}, {H,T} or {S, F} Goal: Observe a H Collection of possible events (Possible subsets of S): {, {1}, {0}, {1,0}=S}

Sample Space and Events Examples: 3)Forecasting the weather for each of the next three days on the Palouse: Experiment: Set of possible outcomes (sample space), S: Observing whether the weather is rainy (R) or not (N) in each of the next three days on the Palouse. {NNN, RNN, NRN, NNR, RRN, RNR, NRR, RRR} Goal: Interested in whether you should bring an umbrella or not.

Sample Space and Events Examples: 3)Forecasting the weather the next three days on the Palouse: Collection of possible events (Possible subsets of S): {, {NNN}, {RNN}, …, {NNN, RNN}, {NNN, NRN},…, {NNN, RNN, NRN},…, {NNN, RNN, NRN, NNR, RRN, RNR, NRR, RRR}=S} {{NNN}, {RNN}, {NRN}, {NNR}, {RRN}, {RNR}, {NRR}, {RRR}} are called the elemintary events. S, is called the sure event (as well as the sample space.)

Sample Space and Events Examples: 4)Forecasting the weather the next three days on the Palouse: Experiment: Set of possible outcomes (sample space), S: Observing the number of rainy days of the next three days on the Palouse. {0, 1, 2, 3} Goal: Just want to know the number of rainy days out of those three.

Sample Space and Events Examples: Some possible events (Possible subsets of S): No rainy days will be observed = {0} Less than one rainy days will be observe = {<1} = {0} 4)Forecasting the weather the next three days on the Palouse: More than one rainy days will be observe = {>1} = {2, 3} At least one rainy day will be observed = {>0} = {1, 2, 3}

Sample Space and Events Examples: 5)Studying the chance of observing the faces of two dice when rolled: Experiment: Set of possible outcomes (sample space), S: Rolling two dice Goal: Observe faces of two dice die1/die (1,1)(1,2)(1,3)(1,4)(1,5)(1,6) 2(2,1)(2,2)(2,3)(2,4)(2,5)(2,6) 3(3,1)(3,2)(3,3)(3,4)(3,5)(3,6) 4(4,1)(4,2)(4,3)(4,4)(4,5)(4,6) 5(5,1)(5,2)(5,3)(5,4)(5,5)(5,6) 6(6,1)(6,2)(6,3)(6,4)(6,5)(6,6)

Some possible events (Possible subsets of S): Die 1 will have face with number 2: {(2,1), (2,2), (2,3), (2,4), (2,5), (2,6)} Sample Space and Events Examples: 5)Studying the chance of observing the faces of two dice when rolled: Die 2 will have face with number 3: {(1,3), (2,3), (3,3), (4,3), (5,3), (6,3)} The simple event: Die 1 and Die 2 will have faces with numbers 3 and 4 respectively: {(3,4)}

Sample Space and Events Examples: 6)Studying the chance of observing the sum of faces of two dice when rolled: Experiment: Set of possible outcomes (sample space), S: Rolling two dice Goal: Observe sum of faces of two dice {2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12} Some possible events (Possible subsets of S): Sum is less than 5: {2, 3, 4} Sum is between 3 and 9: {4, 5, 6, 7, 8}

Sample Space and Events Examples: 7)Studying the chance of observing a smoker and observing that that smoker has lung cancer: Experiment: Set of possible outcomes (sample space), S: Observe individuals and determine whether they smoke and have lung cancer or not Goal: Study association between smoking habits and lung cancer {NN, SN, NC, SC}

Sample Space and Events Examples: All of the above examples had a finite sample spaces; i.e. we could count the possible outcomes. Some possible events (Possible subsets of S): An individual is a smoker: {SN, SC} An individual does not have cancer: {NN, SN} An individual is a non-smoker and has cancer: {NC} 7)Studying the chance of observing a smoker and then observing that that smoker has lung cancer:

Sample Space and Events Section 2.1 Examples: 8)Flipping a coin until the first head shows up: Experiment: Set of possible outcomes (sample space), S: Flipping a coin multiple times and stop when head is observed. Goal: Observe T’s until first H {H, TH, TTH, TTTH, TTTTH, …}

Some possible events (Possible subsets of S): Sample Space and Events Examples: 8)Flipping a coin until the first head shows up: Observing at least two tails before we stop: {TTH, TTTH, TTTTH, …} Observing at most 4 tails before we stop: {H, TH, TTH, TTTH, TTTTH} Observing exactly 2 tails before we stop: {TTH}

Sample Space and Events Examples: 9)Flipping a coin until the first head shows up: Experiment: Set of possible outcomes (sample space), S: Flipping a coin multiple times and stop when head is observed. Goal: Observe number of flips needed to stop. {1, 2, 3, 4, 5, …}

Sample Space and Events Examples: 9)Flipping a coin until the first head shows up: The above two examples had a countably infinite sample spaces; i.e. we could match the possible outcomes to the integer line. Some possible events (Possible subsets of S): Observing at least two tails before we stop: {3, 4, 5, 6, …} Observing at most 4 tails before we stop: {1, 2, 3, 4, 5} Observing exactly 2 tails before we stop: {3}

Sample Space and Events Def : A discrete sample space: is a sample space that is either finite or countably infinite

Sample Space and Events Examples: 10)Flipping a coin until the first head shows up: Experiment: Set of possible outcomes (sample space), S: Flipping a coin multiple times and stop when head is observed. Goal: Observe the time t until we stop in minutes.

The above example has a continuous sample spaces; i.e. the possible outcomes belong to the real, number line. Sample Space and Events Examples: 10)Flipping a coin until the first head shows up: Some possible events (Possible subsets of S): t < 10: [0, 10) : (5, 20]

Sample Space and Events Some relations from set theory and Venn diagrams: 1)The complement of an event A, referred to as A’, is the set of all outcomes in S that do not belong to A. A’ A S

Sample Space and Events Some relations from set theory and Venn diagrams: 1)The complement of an event A, referred to as A’, is the set of all outcomes in S that do not belong to A. The complement of

Sample Space and Events Some relations from set theory and Venn diagrams: 2)The union of two events A and B denoted by is the event consisting of all outcomes that are either in A, in B or in both. A S B

Sample Space and Events Some relations from set theory and Venn diagrams: 2)The union of two events A and B denoted by is the event consisting of all outcomes that are either in A, in B or in both.

Sample Space and Events Some relations from set theory and Venn diagrams: 3)The intersection of two events A and B denoted by is the event consisting of all outcomes that are in both A and B. A S B

Sample Space and Events Some relations from set theory and Venn diagrams: 3)The intersection of two events A and B denoted by is the event consisting of all outcomes that are in both A and B.

Sample Space and Events Some relations from set theory and Venn diagrams: 4)If the intersection of two events A and B,, is the empty set,, then these events are said to be disjoint or mutually exclusive. A S B

Sample Space and Events Some relations from set theory and Venn diagrams: 4)If the intersection of two events A and B,, is the empty set,, then these events are said to be disjoint or mutually exclusive. Also, any two simple events are going to be disjoint!

Sample Space and Events Some relations from set theory and Venn diagrams: 5)Set difference: A – B, is equivalent to. A S B

Sample Space and Events Some relations from set theory and Venn diagrams: 6)Event A is a subset of B,, if every outcome in A is also found in B. B S A

Sample Space and Events Some relations from set theory and Venn diagrams: 6)Event A is a subset of B,, if every outcome in A is also found in B.

Sample Space and Events Section 2.1 Some relations from set theory and Venn diagrams: 7)De Morgan’s Laws: