# Solutions for the exam for Matematisk statistik och diskret matematik (MVE050/MSG810). Statistik för fysiker (MSG820). December 15, 2012.

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1 Solutions for the exam for Matematisk statistik och diskret matematik (MVE050/MSG810). Statistik för fysiker (MSG8). December 15, (3p) The joint distribution of the discrete random variables X and Y is given by the density table: x\y a) Find E[X], E[Y ], E[XY ] b) Find Cov(X, Y ) c) Find E[5X Y + 17] a) E[X] = 1 ( ) + 2 ( ) = 1.4, E[Y ] = 0 ( ) + 1 ( ) = 0.5, E[XY ] = = 0.8 b) Cov(X, Y ) = E[XY ] E[X]E[Y ] = = 0.1 c) E[5X Y + 17] = 5 E[X] E[Y ] + 17 = = (3p) Draw a card at random from a standard 52-card deck. Denote A = {draw a nine}, B = {draw a diamond}, C = {draw a number}. a) Are the events B and C independent? A and C? Why? b) Find P(A B C). c) Remove one card (the Ace of spades) from the deck. Are A and B independent now? Why? a) B and C are independent: P(B C) = P(draw a number which is a diamond) = 9/52, P(B) = 1/4, P(C) = 9/13, so P(B C) = P(B)P(C) a definition of independent events. Similarly, show that A and C are not independent: P(A C) = 1/52 1/52 9/52 = P(A) P(C). b) P(A B C) = P(A B) P(C) = 1/52 9/13 = 1 36.

2 c) No, if we remove one card, A and B are not independent anymore. Now: P(A B) = 1/51 4/51 13/51 = P(A) P(B). 3. (2p) The continuous random variable X has a density f X (x): { x f X (x) = a, < x < 40, 0, otherwise. a) Find the value of a so that f X (x) becomes a proper density function. b) Using the obtained value of a, find the probability P(X < 25). a) For f X (x) to be a proper density function, it must be non-negative and the total area under the curve must be equal to 1: b) so a = f X (x) dx = P(X < 25) = 40 = 25 x a x2 dx = 2a f X (x) dx = = a = 3 16 = x x2 dx = = 600 a = 1, 4. (4p) Assume the total proportion p of red-haired people in Sweden is The dentist examines n = 10 people every day, which you can assume to be independent of each other. a) What is the probability for the dentist to meet at least 2 red-haired individuums during the same day? b) What is the probability to examine at least 1 red-haired person every day during the whole working week (5 days)? c) What is the expected number of days a new dentist has to work before meeting the first red-haired person? a) Denote by X the total number of red-haired people a doctor examines during one day. X can be thought of as a Binomially(n = 10, p = 0.015) distributed random variable. We are interested in the probability: P(X 2) = 1 P(X 1) = 1 P(X = 0) P(X = 1) ( ) ( ) = 1 (0.015) 0 (0.985) 10 (0.015) 1 (0.985) 9 =

3 b) First, find the probability to meet at least one red-haired person during one day. ( ) 10 P(X 1) = 1 P(X = 0) = 1 (0.015) 0 (0.985) 10 = = Now, assuming the different days being independent of each other, we can argue: = P( P(meet at least 1 on every one of 5 working days) 5 {meet at least 1 red-haired person on i-th day}) i=1 = (P(X 1)) 5 = (0.140) c) There is several ways to approach this problem, here is one of them. Denote by Y the number of the first day the doctor meets a red-haired patient. We can see the sequence of days as a sequence of independent Bernoulli(0.140) trials, since the probability to meet at least one red-haired patient during one day is (from part b). Therefore, Y has a Geometric distribution with parameter 0.140, hence EY = = (3p) Bad thoughts are popping up in Peter s head according to a Poisson process with intensity λ = 2 [tph] (thoughts per hour). When bad thoughts are there, Peter becomes uneasy and can not eat. a) It takes Peter 40 minutes to eat his lunch. What s the probability for him to eat the lunch in peace, without any bad thoughts? b) If Peter wakes up 6:30 and has a breakfast at 7, what s the probability for him to get at least 3 bad thoughts before breakfast? a) The amount X of bad thoughts that come to Peter during lunch has a Poisson distribution with parameter λt, where λ = 2 and t = 2/3. We are interested in the probability λt (λt)0 P(X = 0) = e = e 4/ ! b) Again, the amount of bad thoughts X Pois(λt). Now t is 0.5, λt = 1, and we want the probability P(X 3) = 1 P(X 2) = 1 e e 1 e 1 0! 1! 2! = 1 e 1 ( ) = 1 2.5e

4 6. (3p) Find a generating function A(x) of the sequence (a n ), defined by the following recursion: a 0 = 2, a 1 = 2, a n = a n 1 + a n 2 1, n = 2, 3,... Note: you don t have to find the (a n ) itself, just the generating function. Start with writing out the recursion for n = 2, 3,... and multiplying with the corresponding power of x: Sum up: a 2 = a 1 + a 0 1, x 2 a 2 x 2 = xa 1 x + x 2 a 0 x 2 1 a 3 = a 2 + a 1 1, x 3 a 3 x 3 = xa 2 x 2 + x 2 a 1 x x 2 x a 4 = a 3 + a 2 1, x 4 a 4 x 4 = xa 3 x 3 + x 2 a 2 x 2 x 2 x a 2 x 2 + a 3 x 3 + a 4 x = x(a 1 x + a 2 x 2 + a 3 x ) +x 2 (a 0 + a 1 x + a 2 x ) x 2 (1 + x + x ) Now, remembering that n=0 a nx n = A(x) and n=0 xn = 1 1 x, we get: A(x) a 1 x a 0 = x(a(x) a 0 ) + x 2 A(x) x x, or, putting in the values for a 0, a 1 : A(x) 2x 2 = x(a(x) 2) + x 2 A(x) x2 1 x. Combine the terms with A(x) on the left and the rest on the right: so A(x)(1 x x 2 ) = 2 A(x) = x2 1 x 2 2x x 2 (1 x)(1 x x 2 ). 2 2x x2 =, 1 x 7. (4p) Stefan is on a quest to find out the average IQ of a seagull. Omitting details about how he measures the IQ of his involuntary subjects, we present his data: a) Help Stefan construct the 95% confidence interval for µ the actual average IQ of a seagull. b) What s the length of your interval? Based on your estimate for the actual variance, how many seagulls does Stefan have to catch for his 95% confidence interval to be of length <1?

5 a) We assume Stefan s data to be Normal(µ, σ) with some unknown parameters. That means, the quantity T = X µ s/ has t-distribution with n 1 degrees of freedom (in n our case, n = 7). That fact leads us to the formulas for the left and right border of the two-sided 95% CI: L = X s t 6, , R = X s + t 6, Looking up t 6,0.025 = in the table and having X = 6.5 and s = found from the data, we obtain the confidence interval: [5.1386, ] b) The length l = R L of the obtained confidence interval is However, we can decrease that by making more observations: s l = R L = 2t 6,0.025 = = 7.4, n n n so for that quantity to be less than one (assuming the sample standard deviation stays the same) we need n to be greater than (7.4) 2 = 51.9, so n = 52 should be enough. Note: we are overestimating a bit, since we are not taking into account how t n 1,0.025 decreases when we increase n. 8. (5p) Tim started working as a ticket controller for a bus company in the beginning of November. Before this job, he had a feeling that around % of people under 21 years old usually did not have tickets. During first month of his job, out of n = 132 such young people he met, 17 were without tickets. Is there a statistically significant reason to update his old beliefs? Test the hypothesis H 0 : p = 0.2 against the appropriate alternative. Decide if you need a two- or one-sided test. What s the p-value for the test? Can you reject H 0 on a significance level α = 0.01? We are going to conduct a significance test of a hypothesis H 0 : p = 0.2 against the alternative H 1 : p < 0.2 to see if the data collected by Tim is significant enough to update his old belief (% of young people don t buy tickets) to a new one (less than % of young people don t buy tickets). The test statistic we are going to use is ˆp p Z = 0, here p 0 = 0.2 is the default value of p and ˆp is the point estimate for p0 (1 p 0 )/n the proportion, obtained from data. Due to the Central Limit Theorem, under H 0, Z has a distribution very close to Normal(0, 1). Find the value of Z. From the data, ˆp = 17/ , so Z = /132 So, the p-value for the test, obtained from the tables for cdf of a Normal(0,1) random variable, is p-value = 0.07

6 The obtained p-value is small, but the result is not significant on the level α = 0.01, hence we cannot reject H 0 on that significance level. Note, it is also possible to test H 0 against the alternative H 1 : p 0.2, in that case the p-value would double, to become (3p) Barbara is playing roulette in a casino, always putting in the minimal bet of 2 cents. Let us denote by X i the change in her capital after i-th game. One can assume all of the games to be independent of each other, played by the same rules (thus X i s are distributed identically to some random variable X). The expected gain after one game is negative: µ X = 0.06 cents, the standard deviation is σ X = 2 cents. Barbara has 2 dollars, her plan is to play n = 100 games and then quit, no matter what. a) Find the expected value of Barbara s total gain S 100 = X 1 + X X 100. b) Find an approximate probability for her total gain S 100 to be positive. (Hint: use the Central Limit Theorem) a) The expectation of the total gain: ES 100 = E[X 1 + X X 100 ] = E[X 1 ] + E[X 2 ] E[X 100 ] = = = 6 b) By CLT, the distribution of Z = (S µ X )/ 100σ X can be approximated with Normal(0, 1), so P(S 100 > 0) = P(S µ X /10σ X > 100µ X /10σ X ) = P(Z > 10µ X /σ X ) = P(Z > 0.3) The latter can be found from the tables for Normal distribution to be 0.382

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