LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034
B.Sc. DEGREE EXAMINATION – MATHEMATICS, PHYSICS & CHEMISTRY
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FOURTH SEMESTER – APRIL 2006
ST 4201 – MATHEMATICAL STATISTICS
(Also equivalent to STA 201)
Date & Time : 22-04-2006/9.00-12.00 Dept. No. Max. : 100 Marks
Part A
Answer all the questions.
- Define conditional probability of the event A given that the event B has happened.
- If A1 and A2 are independent events with P(A1) = 0.6 and P(A2) = 0.3, find P(A1 U A2), and P(A1 U A2c)
- State any two properties of a distribution function.
- Define the covariance of any two random variables X and Y. What happens when they are independent?
- The M.G.F of a random variable is [(2/3) + (1/3) et]5 . Write the mean and variance.
- Define a random sample.
- Explain the likelihood function.
- Let X have the p.d.f. f(x) =1/3, -1<x<2, zero elsewhere. Find the M.G.F.
- Define measures of skewness and kurtosis through moments.
- Define a sampling distribution.
Part B
Answer any five questions.
- Stat and prove Bayes theorem.
- Derive the mean and variance of Gamma distribution.
- Let the random variables X and Y have the joint pdf
x + y, 0<x<1, 0<y<1
f(x, y) =
0, otherwise.
Find the correlation coefficient.
- A bowl contains 16 chips of which 6 are red, 7 are white and 3 are blue. If 4 chips are taken at random and without replacement, find the probability that
- All the 4 are red.
- None of the 4 is red.
- There is atleast one of each colour.
- State and prove the addition theorem for three events A, B and C. What happens when they are mutually exclusive?
- Derive the mgf of Poisson distribution. And hence prove the additive property of the Poisson distribution.
- Let X1 and X2 denote a random sample of size 2 from a distribution that is N(m, s2). Let Y1 = X1 + X2 , Y2 = X1 – X2. Find the joint pdf of Y1 and Y2 and show that Y1 and Y2 are independent.
- Define the cumulative distribution function F(x) of a random variable X and mention the properties of it.
Part C
Answer any two questions.
- a) Derive the recurrence relation for the central moments of Binomial distribution. Obtain the first four moments.
- b) Show that Binomial distribution tends to poisson distribution under certain conditions. (10 +10 = 20)
- a) Discuss the properties of Normal distribution
- b) In a distribution exactly normal, 10.03% of the items are under 25 kilogram weight and 89.97 % of the items are under 70 kilogram weight. What are the mean and standard deviation of the distribution? (10 +10 = 20)
- Let f(x, y) = 8xy, 0<x<y<1; f(x, y) = 0 elsewhere. Find
- a) E(Y/X = x), b). Var( Y/X = x).
- b) If X and Y are independent Gamma variates with parameters m and v respectively, show that the variables U = X + Y, Z = X / (X + Y) are independent and that U is a g( m + v) variate and Z is a b1(m, v) variate. (10 +10 = 20)
- a) Derive the pdf of t-distribution.
- b) Obtain the Maximum Likelihood Estimators of m and s2 for Normal distribution. (10 + 10 = 20)