Let X be a random variable with finite mean mu and such that E[(X - mu)^2]...
Let X be a random variable with finite mean mu and such that E[(X - mu)^2] is finite. Then the variance of X is defined to be E[(X - mu)^2], denoted as sigma^2. Using this expected value expression: sigma^2 = E[(X - mu)^2], show that the variance, sigma^2 = E(X^2) - mu^2
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Let X be a random variable with finite mean mu and such that
E[(X - mu)^2]...
Let X be a normal random variable with mean µ, variance^2 , pdf and mdf
Let \(X\) be a normal random variable with mean \(\mu\), variance \(\sigma^{2}\), pdf$$ f(x)=\frac{1}{\sigma \sqrt{2 \pi}} e^{-\frac{(x-\mu)^{2}}{2 \sigma^{2}}} $$and mgf \(M(t)=e^{\mu t+\frac{1}{2} \sigma^{2} t^{2}}\)(a) Prove, by identifying the moment generating function of \(a+b X\), that \(a+b X \sim\) \(N\left(a+b \mu, b^{2} \sigma^{2}\right)\)(b) Prove, by identifying the pdf of \(a+b X\) (via the cdf), that \(a+b X \sim N(a+\) \(\left.b \mu, b^{2} \sigma^{2}\right)\)
1 Let X be a discrete random variable. (a) Show that if X has a finite...
1 Let X be a discrete random variable. (a) Show that if X has a finite mean μ. then EX-ix-0. (b) Show that if X has a finite variance, then its mean is necessarily finite 2 Let X and Y be random variables with finite mean. Show that, if X and Y are independent, then 3 Let Y have mean μ and finite variance σ2 (a) Use calculus to show that μ is the best predictor of Y under quadratic...
7.109 Sample variance: Let X be a random variable with finite variance. Supposem don't know the...
7.109 Sample variance: Let X be a random variable with finite variance. Supposem don't know the variance of X and want to estimate it. You take a random sample, A1, sample, X1,..., X from the distribution of X and set S = (n − 1)-'L'=(X; - X)2. Show that the random variable 2-which is called the sample variance based on a sample of size n-is an unbiased estimator of oz.
Let X be a random variable with mean μ and variance σ2, and let Y be...
Let X be a random variable with mean μ and variance σ2, and let Y be a random variable with mean θ and variance τ2, and assume X and Y are independent. (a) Determine an expression for Corr(X Y , Y − X ). (b) Under what conditions on the means and variances of X and Y will Corr(XY, Y −X) be positive (i.e., > 0 )?
Let X be a Poisson random variable with mean λ
Let X be a Poisson random variable with mean λ(a) Evaluate E{X(X −1)} from first principles, and from this, the variance of X. (b) Confirm the variance using the moment generating function of X.
C2.3 Let X and Y be random variables with finite variance, so that EX2o0 (i) Show...
C2.3 Let X and Y be random variables with finite variance, so that EX2o0 (i) Show that E(X) - (EX) E(X - EX)2, and hence that the variance of (ii) By considering (|XI Y)2, or otherwise, show that XY has finite expecta- (iii) Let q(t) = E(X + tY)2. Show that q(t)2 0, and by considering the roots of and EY2 < oo. X is always non-negative. tion the equation q(t) 0, deduce that
Let X~ U(a, b) be a uniformly distributed random variable. Use the definition of mean and...
Let X~ U(a, b) be a uniformly distributed random variable. Use the definition of mean and variance to show that: (a) E(X (b) Var(X) 2
Suppose x is a normally distributed random variable with mu=50 and sigma = 3. find a...
Suppose x is a normally distributed random variable with mu=50 and sigma = 3. find a value of the random variable, call it Xo, such that, 1) P(X ≤ Xo)= 0.8413 2) P(X > Xo)= 0.025 3) P(41≤X<Xo)=0.8630 Please show Work not in Excel!
Recall that the variance of a random variable is defined as Var[X]=E[(X−μ)2], where μ = E[X]....
Recall that the variance of a random variable is defined as
Var[X]=E[(X−μ)2], where μ = E[X]. Use the properties of
expectation to show that we can rewrite the variance of a random
variable X as Var [X]=E[X^2]−(E[X])^2
Problem 3. (1 point) Recall that the variance of a random variable is defined as Var X-E(X-μ)21, where μ= E[X]. Use the properties of expectation to show that we can rewrite the variance of a random variable X as u hare i- ElX)L...
Assume the random variable X is normally distributed with mean mu equals 50 and standard deviation...
Assume the random variable X is normally distributed with mean mu equals 50 and standard deviation sigma equals 7. Compute the probability. Be sure to draw a normal curve with the area corresponding to the probability shaded. P(X>38)=
1 Let X be a discrete random variable. (a) Show that if X has a finite mean μ. then EX-ix-0. (b) Show that if X has a finite variance, then its mean is necessarily finite 2 Let X and Y be random variables with finite mean. Show that, if X and Y are independent, then 3 Let Y have mean μ and finite variance σ2 (a) Use calculus to show that μ is the best predictor of Y under quadratic...
7.109 Sample variance: Let X be a random variable with finite variance. Supposem don't know the variance of X and want to estimate it. You take a random sample, A1, sample, X1,..., X from the distribution of X and set S = (n − 1)-'L'=(X; - X)2. Show that the random variable 2-which is called the sample variance based on a sample of size n-is an unbiased estimator of oz.
C2.3 Let X and Y be random variables with finite variance, so that EX2o0 (i) Show that E(X) - (EX) E(X - EX)2, and hence that the variance of (ii) By considering (|XI Y)2, or otherwise, show that XY has finite expecta- (iii) Let q(t) = E(X + tY)2. Show that q(t)2 0, and by considering the roots of and EY2 < oo. X is always non-negative. tion the equation q(t) 0, deduce that
Let X~ U(a, b) be a uniformly distributed random variable. Use the definition of mean and variance to show that: (a) E(X (b) Var(X) 2
Recall that the variance of a random variable is defined as
Var[X]=E[(X−μ)2], where μ = E[X]. Use the properties of
expectation to show that we can rewrite the variance of a random
variable X as Var [X]=E[X^2]−(E[X])^2
Problem 3. (1 point) Recall that the variance of a random variable is defined as Var X-E(X-μ)21, where μ= E[X]. Use the properties of expectation to show that we can rewrite the variance of a random variable X as u hare i- ElX)L...
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