![L.1) BinomialDist[1, p] random variables In what context do random variables with BinomialDist[1, p] arise? L.2) Expected value and Variance for the Binomial[1, p] and Binomial[n, p] random variables a) Go with a random variable X with BinomialDist[1, p Calculate Expect[X] and Var[X]. b) Go with a random variable X with BinomialDist[n, p]. Use the fact that X is the sum of n independent random variables each with BinomialDist[1, pl to explain why: Expect[x]-n p and Var[X]-np(p) L.3) Relations among BinomialDist[1, P], BinomialDist[n, p] and the normal distributions a) If X1, X2, X3, . . . , Xn are independent random variables all with the BinomialDist[1, p] distribution, then what is the distribution of XI X2 +X3 +... + Xn? b) How does your answer to part a) explain why random variables with the BinomialDist[n, p] are approximately normally distributed for large n?](http://img.homeworklib.com/questions/c43fcb40-7320-11ea-903c-3d3c1c6a75bb.png?x-oss-process=image/resize,w_560)
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L.1) BinomialDist[1, p] random variables In what context do random variables with BinomialDist[1, p] arise? L.2)...
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random variables (Xi,...
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random variables (Xi, X2, X3, ..., Xs, ...] all with the same cumulative distribution function so that μ-Expect[X] = Expect[X] and σ. varpKJ-Var[X] for all i and j Put As n gets large, the cumulative distribution function of S[n] is well approximated by the Normal[0, 1] cumulative distribution function. Another version of the Central Limit Theorem used often in statistics says Go with independent random variables (Xi....
L.11) Brand name distributions a) Give an example of a normally distributed random variable. b) Give...
L.11) Brand name distributions a) Give an example of a normally distributed random variable. b) Give an example of an exponentially distributed random variable. c) Give an example of a random variable with the Weibull distribution. d) Give an example of a random variable with the Pareto distribution. L.4) Sample means from BinomialDist[1, p] IfXl. X2. X3, Xn are independent random samples from a random variable with the BinomialDist[1, p] distribution, then what normal cumulative distribution function do you use...
1. Give the experimental line a real test. Come up with an n so that if...
1. Give the experimental line a real test. Come up with an n so
that if the experimental line produces n chips with failure rate
6/38 or less, then the probability of getting a failure rate 6/38
or less under the original production system is less than 0.01.
2. If two random variables have the same generating function,
must they have the same cumulative distribution function?
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random...
l. X) points Lei Xi, X, X b e random variables . I. adl X, is...
l. X) points Lei Xi, X, X b e random variables . I. adl X, is "uifornly disi rilnicd 。" on [0,1]. The random variables Xi, X2, X3,... are independent. The random variable N is the first integer n 2 1 such that Xn 2 c where 0< c< is a constant. That is, N = min(n : Xn-c). What is EM?
1. Give the experimental line a real test. Come up with an n so that if...
1. Give the experimental line a real test. Come up with an n so
that if the experimental line produces n chips with failure rate
6/38 or less, then the probability of getting a failure rate 6/38
or less under the original production system is less than 0.01.
2. If two random variables have the same generating function,
must they have the same cumulative distribution function?
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random...
L.11) Sums of independent random variables a) If X1 , X2 X, , , Xn are...
L.11) Sums of independent random variables a) If X1 , X2 X, , , Xn are independent random variables all with Exponential μ distribution, then what is the distribution of XII + 2 +X3 + .tX b) If X is a random variable with Exponential[u] distribution, then what is the distribution of x +X1? c) If X1 , X2 , Х, , , X are independent random variables all with Normal 0. I distribution, then what is the distribution of...
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y)...
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
problems binomial random, veriable has the moment generating function, y(t)=E eux 1. A nd+ 1-p)n. Show...
problems binomial random, veriable has the moment generating function, y(t)=E eux 1. A nd+ 1-p)n. Show that EIX|-np and Var(X) np(1-p) using that EIX)-v(0) nd E.X2 =ψ (0). 2. Lex X be uniformly distributed over (a b). Show that ElXI 쌓 and Var(X) = (b and second moments of this random variable where the pdf of X is (x)N of a continuous randonn variable is defined as E[X"-广.nf(z)dz. )a using the first Note that the nth moment 3. Show that...
If two random variables have the same generating function, must they have the same cumulative distribution...
If two random variables have the same generating function, must they have the same cumulative distribution function? L.8) Central Limit Theorem One version of Central Limit Theorem says this: Go with independent random variables (Xi, X2, X3, ..., X.....] all with the same cumulative distribution function so that: 11-Expect[Xi]-Expect[s] and σ. varpk-VarX] for all i and j . Put: s[n] = As n gets large, the cumulative distribution function of S[n] is well approximated by the Normal[o, 1] cumulative distribution...
4. Let Xi, X2,... be uncorrelated random variables, such that Xn has a uniform distribution over...
4. Let Xi, X2,... be uncorrelated random variables, such that Xn has a uniform distribution over -1/n, 1/n]. Does the sequence converge in probability? 5. Let Xi,X2 be independent random variables, such that P(X) PX--) Does the sequence X1 +X2+...+X satisfy the WLLN? Converge in probability to 0?
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random variables (Xi, X2, X3, ..., Xs, ...] all with the same cumulative distribution function so that μ-Expect[X] = Expect[X] and σ. varpKJ-Var[X] for all i and j Put As n gets large, the cumulative distribution function of S[n] is well approximated by the Normal[0, 1] cumulative distribution function. Another version of the Central Limit Theorem used often in statistics says Go with independent random variables (Xi....
L.11) Brand name distributions a) Give an example of a normally distributed random variable. b) Give an example of an exponentially distributed random variable. c) Give an example of a random variable with the Weibull distribution. d) Give an example of a random variable with the Pareto distribution. L.4) Sample means from BinomialDist[1, p] IfXl. X2. X3, Xn are independent random samples from a random variable with the BinomialDist[1, p] distribution, then what normal cumulative distribution function do you use...
1. Give the experimental line a real test. Come up with an n so
that if the experimental line produces n chips with failure rate
6/38 or less, then the probability of getting a failure rate 6/38
or less under the original production system is less than 0.01.
2. If two random variables have the same generating function,
must they have the same cumulative distribution function?
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random...
l. X) points Lei Xi, X, X b e random variables . I. adl X, is "uifornly disi rilnicd 。" on [0,1]. The random variables Xi, X2, X3,... are independent. The random variable N is the first integer n 2 1 such that Xn 2 c where 0< c< is a constant. That is, N = min(n : Xn-c). What is EM?
1. Give the experimental line a real test. Come up with an n so
that if the experimental line produces n chips with failure rate
6/38 or less, then the probability of getting a failure rate 6/38
or less under the original production system is less than 0.01.
2. If two random variables have the same generating function,
must they have the same cumulative distribution function?
L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random...
L.11) Sums of independent random variables a) If X1 , X2 X, , , Xn are independent random variables all with Exponential μ distribution, then what is the distribution of XII + 2 +X3 + .tX b) If X is a random variable with Exponential[u] distribution, then what is the distribution of x +X1? c) If X1 , X2 , Х, , , X are independent random variables all with Normal 0. I distribution, then what is the distribution of...
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
problems binomial random, veriable has the moment generating function, y(t)=E eux 1. A nd+ 1-p)n. Show that EIX|-np and Var(X) np(1-p) using that EIX)-v(0) nd E.X2 =ψ (0). 2. Lex X be uniformly distributed over (a b). Show that ElXI 쌓 and Var(X) = (b and second moments of this random variable where the pdf of X is (x)N of a continuous randonn variable is defined as E[X"-广.nf(z)dz. )a using the first Note that the nth moment 3. Show that...
If two random variables have the same generating function, must they have the same cumulative distribution function? L.8) Central Limit Theorem One version of Central Limit Theorem says this: Go with independent random variables (Xi, X2, X3, ..., X.....] all with the same cumulative distribution function so that: 11-Expect[Xi]-Expect[s] and σ. varpk-VarX] for all i and j . Put: s[n] = As n gets large, the cumulative distribution function of S[n] is well approximated by the Normal[o, 1] cumulative distribution...
4. Let Xi, X2,... be uncorrelated random variables, such that Xn has a uniform distribution over -1/n, 1/n]. Does the sequence converge in probability? 5. Let Xi,X2 be independent random variables, such that P(X) PX--) Does the sequence X1 +X2+...+X satisfy the WLLN? Converge in probability to 0?
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