Probability & Statistics Module 1, Lecture 1

1. Probability & StatisticsModule 1, Lecture 1 Michael Partensky

2. (1) The Laws of Chance… Are they possible? Gambling – it’s serious; On the shoulders of Giants (2) Some applications of probability theory Nature, games, information, economy (3) Are we wired to “do probability”? Paradoxes, misconceptions, tricky question (4) Collecting the data Random experiments, sample spaces, events, operations on sample sets (5) The first definition of probability (P) Axioms and properties of P, Frequency interpretation of P (6) Random variables and distribution function Discrete and continuous variables. Examples. Coins and spinners. (7) First introduction to Mathematica Topics of Discussion (1)

3. Chance, uncertainty, blind fotune... Topic 1: Are the laws of chance possible? Scientific investigation of chance began with the analysis of games of chance. A French nobleman, the Chevalier de Mere, was a gambler who asked his friend, the mathematician Pascal, to explain why some gambling bets made money over time but others consistently lost over time. Pascal wrote to his colleague Fermat and the ensuing correspondence the foundation of probability was laid. Probability initially wasn't considered a serious branch of mathematics because of it's involvement in gambling.

4. On the shoulders of Giants Blaise Pascal, Frenh mathematician and philospher (1623-1662) Pierre de Fermat, French mathematician (1601-1655) Christiaan Huygens, Dutch astronomert, mathematician, physicist (1629-1695) Jacob Bernoulli,Swiss mathematician(1654-1705)

5. Their analysis has given birth to the calculus of probabilities (CP). The central fact of CP: if coin is tossed large number of times, proportions of heads (or tails) becomes close to 50%. In other words, while the result of tossing one coin is completely uncertain, a long series of tosses produces almost certain result. Transition form uncertainty to near certainty is an essential theme in the study of chance.

6. Not only only from their achievements, but also from the errors … D'Alembert argued, that if two coins are tossed, there are three possible cases, namely:(1) both heads, (2) both tails,(3) a head and a tail. So he concluded that the probability of  " a head and a tail " is 1/3. Was he right? If he had figured that this probability has something to do with the experimental frequency of the occurrence of the event, he might have changed his mind after tossing two coins more than few times. Apparently, he never did so. Why? We do not know.

7. Topic 2: Some applications of CP Statistical physics including modeling of biological systems Game theory Decision making in business and economy Information theory Bioinformatics… You name!

8. Topic 3: Are we “wired to do probability”? Our brains are just notwired to do probability problems very well ( Persi Diaconis, Harvard Univ. )

9. A famous statistician would never travel by airplane, because she knew that probability of there being a bomb on any given flight was 1 in a million, and she was not prepared to accept these odds. One day a colleague met her at a conference far from home. "How did you get here, by train?" "No, I flew" "What about the possibility of a bomb?" "Well, I began thinking that if the odds of one bomb are 1:million, then the odds of TWO bombs are (1/1,000,000) x (1/1,000,000) = 10-12. This is a very, very small probability, which I can accept. So, now I bring my own bomb along!" Was she right?

10. Here is a cute brain-stretcher. Adventures on the Moscow Subway* Boris commutes to college by the Moscow subway which runs in a circle. The school happens to be at the point on the circle that is exactly opposite to where Boris boards the train. The trains run in both directions, and the schedule is very regular. The time intervals for trains in both directions are equal; for instance, if there is an hour between the arrival of clockwise (cw) trains, there is also an hour between the arrival of counterclockwise trains. Boris observed, however, that he caught the cwtrains much more often than the ccw trains., despite the fact that his schedule was irregular and he arrived at theStation at random times. Can you explain this? T-station School *From “The chicken from Minks” by Y.B. Chernyak and R.M. Rose

11. Monty Hall problem To switch or not to switch ?

12. Paul Erdos, one of the best mathematician of the 20-th century, allegedly never accepted the MH solution

13. Two envelopes problem The setup: You are given two indistinguishable envelopes, each containing a sum of money. One envelope contains twice as much as the other. Let say the amounts are A and 2A. You pick one envelope at random and open it. Then you're offered the possibility to take the other envelope instead. At this point you do not know if your envelope contains A or 2A. Should you swap the envelopes? Hint:Assume that you can repeat this experiment many times, each time with a new set of envelopes. What would be the average gain from switching?

14. The second daughter Consider two problems • You make a new friend and you ask if she has any children. Yes, she says, two. Any girls? Yes, she says. What is the probability that both are girls? (c)You make a new friend and you ask if she has any children. Yes, she says, two. Any girls? Yes. Next day you meet her with a young girl. Is this your daughter? Yes, she says. What’s the probability that both of her children are girls?

15. Topic 4: Collecting the data Random experiments • Term "random experiment" is used to describe any action whose outcome is not known in advance. Here are some examples of experiments dealing with statistical data: • Tossing a coin • Counting how many times a certain word or a combination of • words appears in the text of the “King Lear” or in a text of Confucius • Counting how many humvees passed the Washington Bridge between 12 and 12.30 p.m. • Counting occurrences of a certain combination of amino acids in a • protein database. • Pulling a card from the deck

16. Sample spaces, sample sets and events The sample space of a random experiment is a set that includes all possible outcomes of the experiment. For example, if the experiment is to throw a die and record the outcome, the sample space is the set of possible outcomes. describes an event that always occurs.Each outcome is represented by a sample point in the sample space. There is more than one way to view and experiment, so an experiment may have more than one associated sample space. For example, suppose you draw one card from a deck. Here are some sample spaces. Sample space 1 (the most common) The space consists of 52 outcomes, 1 for each card. Sample space 2 The space consists of just two outcomes, black and red, Sample space 3 This space consists of 13 outcomes, namely 2,3,4,… 10,J,Q,K,A. Sample space 4. This space consists of two outcomes, picture and non-picture. In tossing a die, one sample space is {1,2,3,4,5,6}, while two others are {odd, even} and {less then 3.5, more then 3.5}

17. The examples above describe the discrete sample spaces. Intuitively, you all understand what it means although a precise mathematical definition would be cumbersome. We will discuss the continuous sample spaces a little later. The examples can be represented by various physical variables, such as weight, height, concentration etc.

18. Try it: • Race between seven horses,1,2,3,4,5,6,7. • Experiment 1 : determining the winner. • Experiment 2: determining the order of finish • Describe the sample spaces

19. Events Certain subsets of the sample space of an experiment are referred to as events. An event is a set of outcomes of the experiment. This includes the null (empty) set of outcomes and the set of all outcomes. Each time the experiment is run, a given event A either occurs, if the outcome of the experiment is an element of A, or does not occur, if the outcome of the experiment is not an element of A. What to consider an event is decided by the experimentalist!

20. In the Sample space of 52 cards the following events can be considered among others: A = “drawing a king”, B = “drawing Q or A”, C = “drawing a non-picture”, D = “drawing a black”. For example, event D consists of 26 points. D is also an event in sample space 2 (consisting of 1 point). It is not an event in sample spaces 3 and 4 (p. 9). Similarly, A (“king”) is an event in sample spaces 1 (4 points) and 3 but not in 2 and 4. Sample space 1The space consists of 52 outcomes, 1 for each card. Sample space 2The space consists of just two outcomes, black and red, Sample space 3 This space consists of 13 outcomes, namely 2,3,4,… 10,J,Q,K,A. Sample space 4This space consists of two outcomes, picture and non-picture.

21. The examples of sample spaces and events Example 1.1 Flip two coins. Try to figure out what is the sample space for this experiment How many simple events does it contain? The answer could be described by a following table:

22. Example 1.2 Role two dice. For convenience we assume that one is red and the other is green (we often use this trick which implies that we can tell between two objects which is which). The following table describes

23. Example 1.2 (continued) . Any event corresponds to some collection of the cells of this table. We show here four different events (colored blue, red, green and gray) . Try to describe them in plain English

24. Example 1.2 (continued) . And what about the following events?Which of these event (red, orange, purple, etc) occurs more often?

25. Example 1.3 An experiment consists of drawing two balls from the box of balls numbered from 1 to 5. Describe the sample space if • The first ball is not replaced before the second is drawn. • The first ball is replaced before the second is drawn. • Example 1.4 Flip three coins. Show the sample space. What is the total number of all possible outcomes?

26. Composite events Quite often we are dealing with composite events. Example. We study a group of students, picking them at random and considering the following events: A = “A student is female”, B=“A student is male”, C= “A student has blue eyes”, D=“A student was born in California”. After this information was collected and the probabilities of A. B, C and D were determined, we decided to find the probabilities of some other events: U= “Student is female with blue eyes”, V=“Student is male,orhas blue eyesandwas not born in California”, etc. The latter questions are dealing with the events that are composed from the “atomic” events A, B, C and D. To describe them, it is convenient to use a language of the Set Theory. Warning: please, don’t be scared. We are not going to be too theoretical. The new symbols will be introduced for our convenience.

27. A B Operations on sample sets • Inclusion: A is a subset of B • “Occurrence of A implies the occurrence of B” or “A is a subset of B” • Example: B = {2,3,5,6}, A={2,6} • “Boys” is a subset of “Males” A B AB Intersection: belongs to A and B *Examples: (1) B = {2,3,4,6}, A={1,2,5,6} ={2,6}; (2) A=“Female students”, B=“Students having blue eyes”, = “Female students with blue eyes”. Usually, instead of the multiplication sign is used: or simply .

28. B A AB A B The empty set is the event with no outcomes. The events are disjoined if they do not have outcomes in common: Example: A={1,3,4}, B= {2,6} The union of events A and B, is the event that A or B or both occur. Example: A = “Male student”, B= “Having blue eyes”, =“Students who are either male or have blue eyes (or both)” Complementary (opposite) eventsAc isthecompliment to A if and W A Ac

29. Try it: • Let A be the event that the person is male, B that the person is under 30, and C that the person speaks French. • Describe in symbols: • A male over 30; • Female who is under 30 and speaks French • A male who either is under 30 or speaks French

30. The first definition of probability Topic 5: Probability, its definition, axioms and properties We have introduced some important concepts: • Experiments, • Outcomes, • Sample space, These concepts will come to life after we introduce another crucial concept- probability, which is a way of measuring the chance. A probability is the way of assigning numbers to events that satisfies following conditions or …

31. 5.1 Axioms and properties of probability Topic 5: Probability, its definition, axioms and properties We have introduced some important concepts: • Experiments, • Outcomes, • Sample space, These concepts will come to life after we introduce another crucial concept- probability, which is a way of measuring the chance. A probability is the way of assigning numbers to events that satisfies following conditions or …

32. Impossible event sure event The Axioms of Probability (1) For any event A (1.1) (2) If is the sample space, then   (1.2) (3) For a sequence of disjoined (incompatible) events Ai (finite or infinite), In other words, the probability for a union of disjoined events equals the sum of individual probabilities. For 2 incompatible events (1.3) gives We will introduce here another important property, although it is not an Axiom and will be justified later in terms of the conditional probability .

33. (4) If A and B are independent, then In other words, for any number of independent events, N,

34. AB A B Some other properties of probability. Deduction from the axioms 1-3: Try to prove (a) and (b). The property (c) is harder to prove formally, although intuitively it is quite clear. Summing up the areas of A and B, one counts their intersection twice. The probability is kind-a proportional to the area. Therefore, one of the occurrences of AB should be removed.

35. We introduced two new concepts: incompatible (disjoined) and independent events • Two events are said to be incompatible or disjoined if they can not occur together. • Two events are said to be independent if they have “nothing to do” with each other.

36. Examples: • (Disjoined) Jack can arrive to Boston either on Monday (“M”) or on Wednesday (“W”). These events can not occur together (he can not arrive on Monday and on Wednesday). Therefore the events are disjoined. If P(M)=0.72 and P(W)=0.2, thenP(M or W)=0.92 (there is still a chance that Jack won’t come to Boston at all). • (Independent) A= “It will be sunny today”, B=“Celtic will win the game tomorrow”. What is the probability that both events will occur simultaneously?P(A and B)=P(A) P(B). • Please,offer some more examples of both kinds. You can find a very useful and provocative discussion of these concepts and of the probability in general in the book “Chance and Chaos” by David Ruelle (Princeton Sci. Lib.)

37. Comment In many cases it is easier to find a probability of a compliment event Ac rather then A itself. It such cases, the rule (1.7) can be very helpful. Example: Toss two dice. Find the probability that they show different values, for example (4,6) and (2,3) but not (2,2). You can count favorable outcomes directly, or better still, by (3) P(non-matching dice) = 1 –P(matching dice) = 1 – 6/36 = = 5/6. We will be using this trick quite often.

38. Example: the classical “birthday problem”. • Find a probability that in a group of n people at least two will have the same birthday. • You will solve a version of this problem at home. • Let’s discuss here a simpler problem. • N people arrive to a vacation place during the same week. Each one chooses randomly his/her arrival day. What is the probability that at least two people arrive on the same day? • Let’s discuss it together (working in groups) • What is the complimentary event? • Consider first N=2, • N=8. • Now let us check N=3. • The general case

39. 5.2 Two definitions of probability Combinatorial definition If a random experiment has Nequally possibleoutcomes of which NA outcomes correspond to an event A, then

40. Frequency definition (empirical) If we repeat an experiment a large number of times, then the fraction of times the event A occurs will be close to P(A): In other words, if N(A,n) is the number of times that event occurs in the first n trials, then It's easy to prove that defined this way, P(A) satisfies conditions (1) and (2) Try it ! Hint: the property (3) follows from

41. Random Variables and the Distribution Function. The simplest experiments are flipping coins and throwing dice. Before we can say anything about the probabilities of their various outcomes (such as "Getting an even number" on the die, or "Getting 3 heads in 5 consecutive experiments with a coin") we need to make a reasonable guess about the probabilities of the elementary events (getting H or T for a coin , or one of six faces for a die). For instance, we can assume (as we usually do) that a die is perfectly balanced and all faces are equivalent. Then , the probability of any number equals 1/6. As we will see, it can be described in terms of distribution (because it distributes probabilities between different outcomes) function. The term “function”, however, implies some arguments (or variables). It would be inconvenient to use a “function of faces”, or a “functions of Heads or Tails”. That’s why we will introduce a general term for describing various random outcomes.

42. Random Variables We now introduce a new term Instead of saying that the possible outcomes are 1,2,3,4,5 or 6, we say that random variable X can take values {1,2,3,4,5,6}.A random variable is an expression whose value is the outcome of a particular experiment. The random variables can be either discrete or continuous. It’s a convention to use the upper case letters (X,Y) for the names of the random variables and the lower case letters (x,y) for their possible particular values.

43. Examples of random variables Discrete: (you name !) Continuous: For instance, weights or a heights of people chosen randomly, amount of water or electricity used during a day, speed of cars passing an intersection. Please, add a few more examples.

44. The Probability Function for discrete random variables We assigned a probability 1/6 to each face of the dice. In the same manner, we should assign a probability 1/2 to the sides of a coin. What we did could be described as distributing the values of probability between different elementary events: P(X=xk)=p(xk), k=1,2,… (1.10)It is convenient to introducethe probability function p(x) : P(X=x)=p(x) (1.11) In other words, the probability of a random variable X taking a particular value x Is called the probability function. For x=xk (1.10) reduces to (1.9) while for other values of x, p(x)=0.

45. The probability function should satisfy the following equations : Example: Suppose that a coin is tossed twice, so that the sample space is ={HH,HT,TH,TT}. Let X represent a number of heads that can come up. Find the probability function p(x). Assuming that the coin is fair, we have P(HH)=1/4, P(HT)=P(TH)=1/4, P(TT)=1/4;Then, P(X=0)=P(TT)=1/4; P(X=1)=P(HTTH)=1/4+1/4=1/2. P(X=2)= ¼.

46. The probability function is thus given by the table:

47. Uniform and non-uniform distributions. If all the outcomes of an experiment are equally probable, the corresponding probability function is called uniform. If the contrary is true, the probability function is non-uniform. Example On the face of a die with 6 dots, 5 dots are filled, so that only the central one is left. What is the probability function for this case? The value X=1 will occur in average 2 times more often than in the balanced die. As a result, p(1)=1/3, p(2)=p(3)=p(4)=p(5) = 1/6 . Working together Try It Suppose we pick a letter at random from the word TENNESSEE. What is the sample space and what is the probability function for the outcomes? Challenge: For two dice experiment, find the probability function for X=“ Sum of two throws”. Use the table (it’s reproduced below)

48. Continuous distribution (preliminary remarks)We will repeat this discussion later, with many extra details Suppose that the circle has a unit circumference (we simply use units in which 2 Pi R=1).

49. Continuous distribution. Probability density function (PDF). Suppose that every point on the circle is labeled by its distance x from some reference point x=0. The experiment consists of spinning the pointer and recording the label of the point at the tip of the pointer. Let X is the corresponding random variable. The sample space is the interval [0,1). Suppose that all values of X are equally possible. We wish to describe it in terms of probability. If it was a discrete variable (such as a dice), we would simply assign to every outcome a fixed value of probability to all outcomes.. p(xi)=const. However, for a continuous variable we must assign to each outcome a probability p(x )=0. Otherwise, we would not be able to fulfill the requirement 1.12. Something is obviously wrong!

50. We will come back to the continuous distributions in Lecture 2, after refreshing some Math. Let’s us now open the Mathematica (the rest of the lecture we will practice with M).