# Difference between revisions of "SMHS Probability"

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Answers: e, d, b, b, a, c, c, b, d, b, (■(13@1))(■(4@3))(■(12@1))(■(4@2)),(■(13@2))(■(4@2))(■(4@2))(■(11@1))(■(4@1)),(■(52@5)) , | Answers: e, d, b, b, a, c, c, b, d, b, (■(13@1))(■(4@3))(■(12@1))(■(4@2)),(■(13@2))(■(4@2))(■(4@2))(■(11@1))(■(4@1)),(■(52@5)) , | ||

− | + | (■(13@1))(■(4@3))(■(12@1))(■(4@2)),(■(13@2))(■(4@2))(■(4@2))(■(11@1))(■(4@1)),(■(52@5)) | |

## Revision as of 11:29, 7 July 2014

## Scientific Methods for Health Sciences - Probability Theory

**IV. HS 850: Fundamentals **

Probability Theory

1) Overview: Probability theory plays an important role in statistics and its application in many other areas because it provides the theoretical groundwork for statistical inference. Probability theory is concerned with probability, which is the analysis of random phenomena. The central objects are random variables, stochastic processes, and events. Consider an individual coin toss, which can be considered to be a random event, if it is repeated many times then the sequence of random events will exhibit certain patterns. And probability theory helps us to study and predict those patterns. Often, probability theory can be further divided into two separate parts of discrete probability distribution and continuous probability distribution, which we’ll study later in the Distribution section. In this section, we aim to study some fundamental concepts in probability theory as well as the probability theory rules we are going to apply in our following studies.

2) Motivation: Consider you are doing an experiment where a number of outcomes are produced. This set of outcomes is called sample space and power set of the sample space includes all different collections of the possible results of the experiment. Suppose we are rolling a fair dice, which has 6 possible outcomes. The sample space is {1, 2, 3, 4, 5, 6}. Event is any collection of the possible results. For example, the collection of possible results of rolling an even number gives the subset of {2, 4, 6} which is an element of the power set of the sample space in this experiment. What if we want to estimate the chance of rolling three 2’s in line or the chance of roll an odd number in an experiment? Probability is a way of assigning every event a value between 0 and 1, which informs us of the chance that the event occurs.

3) Theory

3.1) Random Sampling: A simple random sample of n items is a sample in which very member of the population has an equal chance of being selected and the members of the sample are chosen independently. For example, consider a survey where 100 students are chosen from the total of 5000 students to take the questionnaires and the chance of chosen is the same for each student. This is a simple example of random sampling. An easy application is random number generator.

3.2) Types of probabilities: Probability models have two components: sample space and probabilities.

- Sample space (S) for a random experiment is the set of all possible outcomes of the experiment.
- Event: a collection of outcomes.
- Event occurs if an outcome making up that event occurs.

- Probabilities for each event in the sample space.

- Probabilities may come from models – say mathematical/physical description of the sample space and the chance of each event. An example may be a fair dice tossing game.

- Probabilities may be derived from data – data observations determine the probability distribution. An example may be tossing a coin 50 times and observe the head counts.

- Subject probabilities: combining data and psychological factors to design a reasonable probability table. An example may be the stock market.

3.3) Axioms of probability

- First axiom: the probability of an event is a non-negative real number.

- Second axiom: the probability that some elementary event in the entire sample space will occur is 1. More specifically, there are no elementary events outside the sample space P(S)=1.

- Third axiom: An countable sequence of pair-wise disjoint events E_1,E_2,… satisfies P(E_1∪E_2∪…)=∑_i〖P(E_i)〗.

3.4) Event manipulations:

- Complement: the complement of event A is denoted as A^C or A', it occurs if and only if A does not occur. The sum of A and A^C make up the whole sample space.
- A∪B contains all outcomes in A or B (or both). P(A∪B)=P(A)+P(B)-P(A∩B).
- A∩B contains all outcomes which are in both A and B.
- Mutually exclusive events are events that cannot occur at the same time.
- Conditional Probability: The conditional probability of event A occurring given that event B occurs is P(A│B)=(P(A∩B))/(P(B)). When A and B are independent then giving that B occurs gives no information on the probability of A and P(A│B)=P(A).

- Multiplication rule: P(A∩B)=P(A│B)P(B), in general: P(A_1∩A_2∩A_3∩…∩A_n )=P(A_1 )P(A_1│A_2 )P(A_3│A_1∩A_2 )…P(A_n│A_1∩A_1∩A_2∩A_3∩…∩A_(n-1) ).

- Law of total probability: P(B)=P(B│A_1 )P(A_1 )+P(B│A_2 )P(A_2 )+⋯P(B│A_n )P(A_n) where {A_1,…,A_n} partition the sample space S.
- Invert of the order of conditioning: P(B│A)=(P(A∩B))/(P(A))=(P(A∩B))/(P(B)) (P(B))/(P(A))=P(A│B) P(B)/P(A) . Hence: P(A∩B)=P(A│B)P(B)=P(B│A)P(A).
- Bayesian Rule: If {A_1,…,A_n} partition the sample space S and A and B are any events that are subsets of S then we have:

P(A│B)=(P(B│A))/(P(B))

=(P(B│A)P(A))/(P(B│A_1 )P(A_1 )+P(B│A_2 )P(A_2 )+⋯P(B│A_n )P(A_n)).

3.5) Counting: counting principle is very useful in probability theory. Consider picking 3 students from the total of 26 named A to Z respectively. Permutation: rearrangement of objects in distinguishable sequences. Each unique ordering is called a permutation. For example {A, B, D} are different from {D, A, B}. There are 3!=6 permutations of students A, B and D. Permutation with repetitions (replacement): when the ordering of objects matters and an object can be chosen more than once, then the number of permutations is n^r, where n is the number of objects from which you can choose and r is the number of objects you choose. In our example above, we have 〖26〗^3 permutations with repetitions. Permutation without repetitions (replacement): when the order matters and each object can be chosen only once, then the number of permutation is n(n-1)…(n-r+1)=n!/(n-r)! where n is the number of objects you can choose from and r is the number of objects you choose. In our example above, we have 26*25*24 permutations without repetitions. Combinations: An un-ordered collection of unique objects. In our example above, {A, B, D} are the same as {D, B, A}. Combinations with repetitions (replacement): when the order doesn’t matter and an object can be chosen more than once. Then the number of combinations is (■(n+r-1@r))= ((n+r-1)!)/r!(n-1)!, in our example above we have ((26+3-1)!)/3!(26-1)!=6552 combinations with repetitions. Combinations without repetitions (replacement): when the order doesn’t matter and an object can be chosen only once. Then the number of combinations is (■(n@r))=n!/r!(n-r)!, where n is the number of objects you can choose from and r is the number of objects you choose. In our example, we have (■(26@3)) combinations without repetitions.

3.6) Independence vs. disjointness/mutual-exclusiveness: The events A and B are independent if P(A│B)=P(A), that is P(A∩B)=P(A)P(B). The events C and D are disjoint or mutually-exclusive, if P(C∩D)=0, that is P(C∪D)=P(C)+P(D). These two are different concepts and should not be messed together. Given that if two events are mutually-exclusive, they cannot happen together (P(A│B)=0)) so the occurrence of one gives information about the probability of the other so events that are mutally-exclusive can’t be independent. Consider the poker game, if we know the card we picked randomly is a Queen, then the event that it is a red Queen given it is a Queen and the event that it is a black Queen given it is a Queen is independent. The event that it is a black card is not mutually-exclusive from the event that it is a spade.

4) Applications

4.1) This website (http://wiki.socr.umich.edu/index.php/AP_Statistics_Curriculum_2007_Prob_Simul) introduced on application of probability theory through simulation. Many practical examples require probability computations of complex events. Such calculations may be carried out exactly, using the proper probability rules, or approximately using estimation or simulations. SOCR simulations (http://wiki.socr.umich.edu/index.php/AP_Statistics_Curriculum_2007_Prob_Simul) may be used to compute (approximately) probabilities of various processes and compare these empirical probabilities to their exact counterparts. This article included examples of Ball and Urn Experiment, Binomial Coin Toss Experiment, Card Experiment, Roulette Experiment, and Chuck A Luck Experiment and would be a great source to take practice on simulations using probability theory.

4.2) This website (http://www.probabilitytheory.info) offers a list of interesting articles on the topic of probability theory. It included a general introduction to the history of probability theory and addresses a wide list of articles of the application of probability in different areas including business, medicine, economics, biology, and etc. These short articles would be a good start to learn about application of probability theory in various fields.

5) Software
http://www.socr.ucla.edu/htmls/SOCR_Experiments.html
http://www.calculatorsoup.com/calculators/discretemathematics/combinations.php
http://www.calculatorsoup.com/calculators/discretemathematics/permutations.php
http://ww2.coastal.edu/kingw/statistics/R-tutorials/proport.html

6) Problems

6.1) A box contains 6 balls, where 2 are red, 2 are white, and 2 are blue. Four balls are picked at random, one at a time. Each time a ball is picked, the color is recorded, and the ball is put back in the box. If the first 3 balls are red, what color is the fourth ball most likely to be? (a) Red (b) White (c) Blue (d) Blue and white are equally likely and more likely than red. (e) Red, blue, and white are all equally likely.

6.2) A coin is tossed 400 times and 170 heads are observed. This coin is__ ? (a) fair, because the probability of seeing that amount of heads or less is approximately 0.0013 (b) neither fair or unfair. There is not enough information to determine that. (c) fair, because the probability of seeing that amount of heads or less is approximately 0.5 (d) not fair, because the probability of seeing that amount of heads or less is close to 0.

6.3) If two events are independent, then they are automatically mutually exclusive. (a) True (b) False

6.4) If two events are mutually exclusive, then the sums of their probabilities is 1. (a) True (b) False

6.5) A professor who teaches 500 students in an introductory psychology course reports that 250 of the students have taken at least one introductory statistics course, and the other 250 have not taken any statistics courses. 200 of the students were freshmen, and the other 300 students were not freshmen. Exactly 50 of the students were freshmen who had taken at least one introductory statistics course. If you select one of these psychology students at random, what is the probability that the student is not a freshman and has never taken a statistics course? (a) 30% (b) 40% (c) 50% (d) 60% (e) 20%

6.6) A professor who teaches 300 students in an introductory psychology course reports that 135 of the students have taken exactly one introductory statistics course, 60 have taken two or more introductory statistics courses, and the other 105 have not taken any statistics courses. If you select one of these psychology students at random, what is the probability that the student has taken at least one statistics class? (a) 0.20 (b) 0.45 (c) 0.65 (d) 0.35

6.7) In a carnival game, a person can win a prize by guessing which one of 5 identical boxes contains the prize. After each guess, if the prize has been won, a new prize is randomly placed in one of the 5 boxes. If a person makes 4 guesses, what is the probability that the person wins a prize exactly twice?

(a) (0.2)^2/(0.8)^2

(b) 2(0.2)^2*(0.8)^2 (c) 6(0.2)^2*(0.8)^2 (d) (0.2)^2*(0.8)^2 (e) 2!/5!

6.8) In a university with 20,000 students, 20% are engineering students, 40% are in the sciences, 30% are in the social sciences, and the rest have other majors. The counselors in the registrar's office want to survey the opinions of students on the issue of posting grades on-line and they seek opinions from students of various majors. They conduct a survey by randomly selecting students. Among the first three students selected, what is the probability that two of the three major in social sciences and one has a major other than social science?

(a) 0.600

(b) 0.189 (c) 0.090 (d) 0.063

6.9) Every five years the Conference Board of Mathematical Sciences surveys college math departments. In a recent report, 51% of all undergraduates taking calculus were in classes using graphing calculators and 31% were in classes using computer assignments. Suppose that 16% of these students use both calculator and computer. What proportion of undergraduates taking calculus use no technology? (a) 0.44 (b) 0.82 (c) 0.66 (d) 0.34 (e) 0.16

6.10) Two cards are dealt to you (without replacement) from an ordinary well-shuffled deck. Let X = the probability that you have a pair. Let Y = the probability that both of your cards are diamonds. Compare X and Y. (a) X < Y (b) X = Y (c) X > Y

6.11) Poker game: What is number of hands of Full house where you have patterns like AAABB and A and B are from distinct kinds? What is number of hands of two pairs where you have patterns like AABBC and A, B and C are distinct kinds? What is total number of 5-car hands?

7) References

http://mirlyn.lib.umich.edu/Record/012403078 http://mirlyn.lib.umich.edu/Record/000257026 http://wiki.stat.ucla.edu/socr/index.php/Probability_and_statistics_EBook#Chapter_III:_Probability

Answers: e, d, b, b, a, c, c, b, d, b, (■(13@1))(■(4@3))(■(12@1))(■(4@2)),(■(13@2))(■(4@2))(■(4@2))(■(11@1))(■(4@1)),(■(52@5)) ,

(■(13@1))(■(4@3))(■(12@1))(■(4@2)),(■(13@2))(■(4@2))(■(4@2))(■(11@1))(■(4@1)),(■(52@5))

- SOCR Home page: http://www.socr.umich.edu

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