1 / 29

nwfsc.noaa/publications/survey/2001/2001fig1.html

http://www.nwfsc.noaa.gov/publications/survey/2001/2001fig1.html. Remember empirical (measured) frequency distributions. Probability distribution : theoretical frequency distribution, the distribution that you expect to see based on theory or other knowledge.

cato
Télécharger la présentation

nwfsc.noaa/publications/survey/2001/2001fig1.html

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. http://www.nwfsc.noaa.gov/publications/survey/2001/2001fig1.htmlhttp://www.nwfsc.noaa.gov/publications/survey/2001/2001fig1.html Remember empirical (measured) frequency distributions

  2. Probability distribution: theoretical frequency distribution, the distribution that you expect to see based on theory or other knowledge If your data do not conform to the exception it can suggest biological mechanism

  3. Basic Probability Read very carefully S&R 5.1 or Zar Ch 5 S&R is better on this topic Important concepts How to calculate probabilities Difficulty in obtaining random and impendent samples

  4. Probability deals with experiments that can be performed over and over again with individual outcomes that are unpredictable, but whose long term average outcomes can be determined When these long term averages are turned into percentages they are called probabilities The set of all possible outcomes is called the sample space and any subset of the sample space is called an event. http://www.scit.wlv.ac.uk/university/scit/maths/calculus/modules/topics/precalc/probabil/learn.htm

  5. Example: trapping students -4 kinds of students AU (70%), AG (26%), FU (1%), FG (3%) -Proportions are known from enrollment record. -Rarely know. -What proportions would you get if you “trapped” at different locations on campus?

  6. So, how does one take a random (all members of population equally likely to be sampled) sample of people, fish, trees, etc………… If we could randomly sample students we would expect ~ 3% of them to be FG. Probability ranges from 0-1 P[FG]=0.03

  7. The 4 categories of students encompass the set “Trapping” one or a group of students is an event Trapping 3 students could yield the event {AU, AG, FU} Trapping any American student includes AU and AG, call it A Trapping a graduate student includes AG and FG, call it B The intersection of A and B written A  B includes only the events that are shared by A and B……. So only AG The union of A and B includes events which are either A or B or both, written A  B……. So {AG, AU, FG}

  8. What is the probability of trapping an AG? 0.26 What is the probability of trapping a student who is either American or a graduate student? P[A  B]= P[A]+ P[B]- P[A  B] P[A  B]=P{AU,AG}+P{AG,FG)-P{AG} P[A  B]= (0.70+0.26)+(0.26+0.03)-0.26 P[A  B]= 0.99 don’t include twice Could have figured this out by knowing that P[FU] was 0.01!

  9. Up till know, talking about “sampling” one student. If we want to catch 2, we must catch one first and then a second. Must decide whether or not to replace the first one In very large populations, it doesn’t really matter. Are the two students caught independent? Doesn’t relate to whether mom & dad still pay tuition! Does the probability of catching student 1, affect the probability of catching student 2?

  10. Events are independent if: P[DB]= P[D] P[E] P[intersection of D and E] = P[D] P[E] P [events shared by D and E] = P[D] P[E] (in English) the P of one event has no effect on the P of the second event

  11. So……if events are independent, the probability of each events are multiplied to determine the joint probability The probability of catching 2 FG’s is P [FG]* P[FG] 0.03*0.03=.0009 (very small) In the above case, it doesn’t matter which FG you catch first (for our purposes) one FG is as good as another

  12. A dresser drawer contains one pair of socks of each of the following colors: blue, brown, red, white and black. Each pair is folded together in matching pairs. You reach into the sock drawer and choose a pair of socks without looking. The first pair you pull out is red -the wrong color. You replace this pair and choose another pair. What is the probability that you will choose the red pair of socks twice? P [red red]= (0.2*0.2)=0.04 But, P [red] is always 0.2 (if you put the socks back) Every time you draw, P=0.2. Only when you talk about a specific string are the Ps multiplied Hint: don’t put the red sox back in if you don’t want to choose them again. Alternatively open you eyes while getting dressed.

  13. Patient: Will I survive this risky operation? • Surgeon: Yes, I'm absolutely sure that you will survive the operation. • Patient: How can you be so sure? • Surgeon: Well, 9 out of 10 patients die in this operation, and yesterday my ninth patient died. If deaths are independent, the patient still has a 90% chance of dying!

  14. Distinguishable events What is the P of catching an AU and an FG in your sample? Is it P[AU]*P[FG]? 0.7*0.03? NO You could trap (AU FG) or (FG AU), there are 2 ways to do this, so P of catching an AU and an FG is 2 P[AU]*P[FG] 2 * 0.7 * 0.03=0.0420

  15. A small tangent…….. When does order matter? Combinations vs. Permutations Combination: order does not matter Permutation: order does matter Combinations locks are misnamed. They are really permutation locks.

  16. Permutation: An ordered selection of k objects from a set of n objects is called a permutation of n objects, k at a time The following formula can be used to count permutations. n! (n-k)! 5! = five factorial = 5*4*3*2*1=120 http://www.scit.wlv.ac.uk/university/scit/maths/calculus/modules/topics/precalc/probabil/learn.htm

  17. Combination: A selection of k objects from a set of n objects is called a combination of n objects, k at a time The following formula can be used to count combinations. n! (n-k)! k!

  18. Another small tangent…….. Conditional probability: The occurrence of one event is somehow dependent on the occurrence of another event. In terms of our student trapping study, what is the probability of being a graduate student, given that the student is American? P [intersection of A and B] P[A  B] P A, given B P[AlB]= = = P[B] P[B] P[AG] 0.26 = P[AlB]= P[american] 0.96

  19. A generalized version of conditional probability is Bayes’ theorem Very trendy in statistics More on topic later

  20. The binomial distribution is used when there are exactly two mutually exclusive outcomes of a trial. These outcomes are appropriately labeled "success" and "failure". The binomial distribution is used to obtain the probability of observing x successes in N trials, with the probability of success on a single trial denoted by p. The binomial distribution assumes that p is fixed for all trials. Such a success/failure experiment is also called a Bernoulli experiment or Bernoulli trial. Must simplify previous sampling to consider only whether students are foreign or American (ignore grad vs UG)

  21. binomial distribution = only 2 options In the probability space {p, q} p=[F] and q=P[A] So, compute the probability space of samples of 2 students {FF, FA, AA} {p2, 2pq, q2} The probability of catching 2 FG’s is P [FG]* P[FG] 0.03*0.03=.0009 (very small) P of catching an AU and an FG is 2 P[AU]*P[FG] 2 * 0.7 * 0.03=0.0420

  22. Look at pattern: Sample 3 Sample 2 {FFF, FFA, FAA, AAA} {FF, FA, AA} {p3, 3p2 q, 3pq2, q3} {p2, 2pq, q2} .The formula for expanding the binomial function is:  where

  23. Ex pg 73 S & R Insect population with 40% infection. Sample 5 insects. What distribution of samples is expected ? P infection independent for each insect. p = P [infected] = 0.04 q = P [not infected] =0.06 {p5, 5p4 q, 10 p3q2, 10 p2q3 , 5pq4, q5} Probability of 5 “clean” insects Probability of 5 infected insects

  24. Insect population with 40% infection. Sample 5 insects. What distribution of samples is expected ? P infection independent for each insect. p = P [infected] = 0.04 q = P [not infected] =0.06 {p5, 5p4 q, 10 p3q2, 10 p2q3 , 5pq4, q5} (0.4)5 + 5(0.4)4 (0.6) + 10(0.4)3 (0.6)2 + 10 (0.4)2 (0.6)3 + 5(0.4) (0.6)4 + (0.6)5 Probability of 5 “clean” insects Probability of 5 infected insects

  25. Combine probabilities with number of samples taken • If you took 5 insects from the population x times…. • Predict the number of times you would get 5 infected……….5 clean

  26. 900 800 expected 700 600 frequency 500 400 300 200 100 0 zero one two three four five number infected insects

  27. Compare your expected frequency to a real, observed frequency when you actually collect the samples How well do they match up?

  28. Ex pg 74 S & R 900 800 expected 700 observed 600 frequency 500 400 300 200 100 0 zero one two three four five number infected insects

  29. There are tests for determining whether observed frequencies differ from expected by more than can be attributed to chance alone (maybe covered later). For now, think about it visually. Usually, we don’t know p and q If you have a theoretical basis for p and q (eg. 50:50 sex ratio) you can compare observed to predicted

More Related