If we know that E[Y|X] = E[Y], then we also know that X and Y are...
If we know that E[Y|X] = E[Y], then we also know that X and Y are independent. [For those of you not used to this notation, E[Y] is the "expectation" of Y. This may have been introduced to you in another Statistics course as m or the population mean. Extending this, E [Y|X] is the expectation of Y given X.]
a)True
b)False
Homework Answers
E[Y/X] is a conditional expectation of Y given X.
ie. 
If X and Y are independent then,
Therefore 
Hence the given statement is true.
Add Answer to:
If we know that E[Y|X] = E[Y], then we also know that X and Y
are...
For variable X, we know that X ~ N(4,0%). If we define Y = log(x), then...
For variable X, we know that X ~ N(4,0%). If we define Y = log(x), then Var(Y) = (log(6)). Is this statement True or False? O True O False
Let X and Y be independent normal random variables with parameters E[X] =ux, E[Y] = uy...
Let X and Y be independent normal random variables with parameters E[X] =ux, E[Y] = uy and Var(X) = x, Var(Y) = Oy. Indicate whether each of the following statements is true or false. Notation: fx,y (x, y), fx(x), fy (v) denote the joint and marginal PDFs of X and Y , respectively; $(x) is the CDF of a standard normal random variable with zero mean and unit variance. E[XY]=0
please do asap For variable X, we know that X ~ N(4,0%). If we define Y...
please do asap
For variable X, we know that X ~ N(4,0%). If we define Y = log(x), then Var(Y) = (log(6)). Is this statement True or False? O True O False
Question 1 (1 point) When operationalizing your dependent and independent variables, it is important to know...
Question 1 (1 point) When operationalizing your dependent and independent variables, it is important to know the scale of measurement for each. Question 1 options: True False Question 2 (1 point) When running descriptive statistics, those you are most interested in are __________________. Question 2 options: Mean, standard deviation, minimum, maximum, N, and N missing Mean, median, mode, variance and standard deviation Mean, standard error of the mean, and coefficient of variation Range, Skewness, First quartile. Question 3 (1 point)...
1. Let X and Y be two random variables.Then Var(X+Y)=Var(X)+Var(Y)+2Couv(X,Y). True False 2. Let c be...
1. Let X and Y be two random variables.Then Var(X+Y)=Var(X)+Var(Y)+2Couv(X,Y). True False 2. Let c be a constant.Then Var(c)=c^2. True False 3. Knowing that a university has the following units/campuses: A, B , the medical school in another City. You are interested to know on average how many hours per week the university students spend doing homework. You go to A campus and randomly survey students walking to classes for one day. Then,this is a random sample representing the entire...
i need help with 2b please is a set of input values, Y- 2. In this question, we reuse the notation of lecture 37: X-{xi, ,x , m-1) is a set of hash values, and H is an [X → Y)-valued random variab...
i need help with 2b please
is a set of input values, Y- 2. In this question, we reuse the notation of lecture 37: X-{xi, ,x , m-1) is a set of hash values, and H is an [X → Y)-valued random variable {0.1, In lecture, we showed that for any hash value y e Y, the expected number of input values that hash to y is k/m, where k XI and m Yl. However, in determining the time it...
4. Conditional Independencies in Bayes Nets: A E M A A M (ii) (iii) (i) The Bayesian networks in Figure above are all p...
4. Conditional Independencies in Bayes Nets: A E M A A M (ii) (iii) (i) The Bayesian networks in Figure above are all part of the alarm network introduced in class and in Russell and Norvig. We use the notation XIY to denote the variable X being independent of Y, and XIY |Z to denote X being independent of Y given Z d. Similarly, prove that BLM |A in the Bayesian network of Fig. (ii), and MIJ|A in the Bayesian...
5. Suppose we have two random variables X and Y. They are discrete and have the...
5. Suppose we have two random variables X and Y. They are discrete and have the exact same distribution and also independent. You see below the distribution of X which of course also the distribution of Y as well, that is what we called independent and identically distributed) P(X =- X. Remem- a./ (-) Find and draw the cumulative distribution function F() function of ber that F(x) -P(X S) HINT: For the next 3 parts you might want to make...
kindly assist me to know how to find the E(x)=1.82...and E(Y)=1.16 Two descrebre rov X and...
kindly assist me to know how to find the
E(x)=1.82...and E(Y)=1.16
Two descrebre rov X and Y have the following point p af x = 0, 1, 2 k (2x²y + 2y-x²-a f locry) = o otherwise y=113 Debemone 1 K (ii) bxy 1 Cor (XN) (iii) M (tutz) Use MCG, te) to confirm your answer to (11) Above Also assist here please? () Mcbrita) Use MCG, tad to confirm your answer to (ii) Abe Also assoab here please? f(x,y)...
Let X, Y be independent random variables with E[X] = E[Y] = 0 and ox =...
Let X, Y be independent random variables with E[X] = E[Y] = 0 and ox = Oy = 5. Then Var(2x+3Y) = 1. True False
For variable X, we know that X ~ N(4,0%). If we define Y = log(x), then Var(Y) = (log(6)). Is this statement True or False? O True O False
Let X and Y be independent normal random variables with parameters E[X] =ux, E[Y] = uy and Var(X) = x, Var(Y) = Oy. Indicate whether each of the following statements is true or false. Notation: fx,y (x, y), fx(x), fy (v) denote the joint and marginal PDFs of X and Y , respectively; $(x) is the CDF of a standard normal random variable with zero mean and unit variance. E[XY]=0
please do asap
For variable X, we know that X ~ N(4,0%). If we define Y = log(x), then Var(Y) = (log(6)). Is this statement True or False? O True O False
i need help with 2b please
is a set of input values, Y- 2. In this question, we reuse the notation of lecture 37: X-{xi, ,x , m-1) is a set of hash values, and H is an [X → Y)-valued random variable {0.1, In lecture, we showed that for any hash value y e Y, the expected number of input values that hash to y is k/m, where k XI and m Yl. However, in determining the time it...
4. Conditional Independencies in Bayes Nets: A E M A A M (ii) (iii) (i) The Bayesian networks in Figure above are all part of the alarm network introduced in class and in Russell and Norvig. We use the notation XIY to denote the variable X being independent of Y, and XIY |Z to denote X being independent of Y given Z d. Similarly, prove that BLM |A in the Bayesian network of Fig. (ii), and MIJ|A in the Bayesian...
5. Suppose we have two random variables X and Y. They are discrete and have the exact same distribution and also independent. You see below the distribution of X which of course also the distribution of Y as well, that is what we called independent and identically distributed) P(X =- X. Remem- a./ (-) Find and draw the cumulative distribution function F() function of ber that F(x) -P(X S) HINT: For the next 3 parts you might want to make...
kindly assist me to know how to find the
E(x)=1.82...and E(Y)=1.16
Two descrebre rov X and Y have the following point p af x = 0, 1, 2 k (2x²y + 2y-x²-a f locry) = o otherwise y=113 Debemone 1 K (ii) bxy 1 Cor (XN) (iii) M (tutz) Use MCG, te) to confirm your answer to (11) Above Also assist here please? () Mcbrita) Use MCG, tad to confirm your answer to (ii) Abe Also assoab here please? f(x,y)...
Let X, Y be independent random variables with E[X] = E[Y] = 0 and ox = Oy = 5. Then Var(2x+3Y) = 1. True False
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