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As in Examples 1 and 2, use (a) the binomial distribution; (b) the corresponding normal approximation, to find the probabilities of each of the following:
Verify equations ( 8.6 ). Hints: In
What is
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- - (Sec. 6.1) Using a long rod that has length µ (unknown), you are going to lay out a square plot in which the length of each side is µ. Thus the area of the plot will be µ². However, because you do not know the value of µ, you decide to make n independent measurements X1,...,X, of the length. Assume that each X; has mean µ and variance o². (a) Show that X² is not an unbiased estimator for the area of the square plot µ². [Hint: for any rv Y, E[Y²] = V[Y] + E[Y]². Apply this for Y = X.] (b) For what value of k is the estimator X² – kS² unbiased for µ²?arrow_forwardGiven E(x)= 4.5. Find a. the value of p and q b. variance 2. For the continuous probability function f(x) = 2kx² e-* when x 2 0. Find a. value of k b. meanarrow_forwardCheck whether the mean and the variance of the following distributions exist: a a. fx (x) = -00 < x< 0 (a is positive constant) T(a²+x²) b. fx(x) = {2* {2x3 x21 elsearrow_forward
- The mean and variance of a Binomial variable X with parameters n and p are 16 and 8. Find P(X> or = 1) and P(X>2).arrow_forwardQ.7 For an exponential distribution fx(x; 4) = (ue Hx: x 20 %3D where u > 0 Write (Don't derive) (a) Mean (expected value), i.e. E(X). (b) Standard deviation, i.e. S.D. (X). (c) Moment generating function, i.e. Mx(t). (d) Cumulative distribution function (cdf), Fx(x). of Nizwarrow_forward1. Let X be a random variable having pdf f(x) = 6x(1 – x) for 0 < I < 1 and 0 elsewhere. Compute the mean and variance of X. 2. Let X1, X2,..., X, be independent random variables having the same distribution as the variable from problem 1, and let X, = (X1+ ·.+Xn). Part a: Compute the mean and variance of X, (your answer will depend on n). Part b: If I didn't assume the variables were independent, would the calculation in part a still work? Or would at least part of it still work? 3. Suppose that X and Y are both independent variables, and that each has mean 2 and variance 3. Compute the mean and variance of XY (for the variance, you may want to start by computing E(X²Y²)). 4. Suppose that (X,Y) is a point which is equally likely to be any of {(0, 1), (3,0), (6, 1), (3, 2)} (meaning, for example, that P(X = 0 and Y = 1) = }). Part a: Show that E(XY) = E(X)E(Y). Part b: Are X and Y independent? Explain. 5. Let X be a random variable having a pdf given by S(2) = 2e-2" for 0arrow_forwardanswerarrow_forwardA production line manufactures 1000-ohm resistors that have 10% tolerance. Let X denotes the resistance of the resistor. Assuming that X is a Gaussian random variable with mean 1000 and variance 2500, find the probability that a resistor picked at random is rejected. (An answer in terms of function Φ?(?) is OK.)arrow_forwardFor a football game in the National Football League, let y = difference between the number of points scored by the home team and the away team (so y > 0 if the home team wins). Let x bet the predicted difference according to the Las Vegas betting spread. For the 768 NFL games played between 2003 and 2006, output follows. Predictor Coefficient SE Coef T P Constant -0.4022 0.5233 -0.77 0.442 BP 1.0251 0.0824 12.44 0.000 (a) We wish to test the null hypothesis that the Las Vegas predictions are unbiased. This will correspond with ? = 0 and ? = b) Based on the results shown in the table, is there much evidence that the sample fit differs from the model ?y = ? + ?x, with values of alpha and beta above? Why?arrow_forwardQ.7 For an exponential distribution fx(x; p) = (ue H, x20 %3D e. w. where u > 0 Write (Don't derive) (a) Mean (expected value), i.e. E(X). (b) Standard deviation, i.e. S.D. (X). (c) Moment generating function, i.e. Mx(t). (d) Cumulative distribution function (cdf), Fx(x). of Nizvarrow_forwardSuppose that there are two assets that are available for investment and an investor has the following expected utility: EU = E(R,)– 0.5Ao, where expected return and standard deviation are expressed in decimals. For example, if expected return is 25%, standard deviation is 15%, and risk aversion is 5, expected utility is computed as: EU = 0.25 – 0.5×5x 0.15 = 0.1938 Now, assume that there is no other instrument (such as the risk-free security) available. Then, derive the analytical expressions for the optimal portfolio weights of the first and the second assets for this specific investor. (Hint: We are not talking about a numerical response here. Rather, you are asked to derive mathematically how you would compute for the optimal portfolio.)arrow_forwardf X is a random variable such that: E(X) = 6.2 and E(X2) = 62.5, then what is the standard deviation of X?arrow_forwardarrow_back_iosSEE MORE QUESTIONSarrow_forward_ios
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill