Now, suppose that X is a Bernoulli random variable with success probability Pr (X = 1) = p. Use the information above to answer the following questions. Show that E(X") =p.

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Author:Amos Gilat
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Chapter1: Starting With Matlab
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Observe that for a random variable Y that takes on values 0 and 1, the expected value of Y is defined as follows:
E(Y) = 0×PP(Y= 0) + 1× Pr(Y= 1)
Now, suppose that X is a Bernoulli random variable with success probability Pr (X = 1) = p. Use the information above to answer the following questions.
%3D
Show that E
(x) = p
E(x°) =
+ ( xp) =
(Use the tool palette on the right to insert superscripts. Enter you answer in the same format as above.)
Transcribed Image Text:Next Question Observe that for a random variable Y that takes on values 0 and 1, the expected value of Y is defined as follows: E(Y) = 0×PP(Y= 0) + 1× Pr(Y= 1) Now, suppose that X is a Bernoulli random variable with success probability Pr (X = 1) = p. Use the information above to answer the following questions. %3D Show that E (x) = p E(x°) = + ( xp) = (Use the tool palette on the right to insert superscripts. Enter you answer in the same format as above.)
Let X be the number of applicants who apply for a senior level position at a large multinational corporation. The probability distribution of the random variable X is given in the following table. The outcomes (number of applicants) are mutually
exclusive.
Complete the table by calculating the cumulative probability distribution of X.
Outcome (Number of applicants)
1
2
4
Probability distribution
0.35
0.30
0.12
0.15
0.08
Cumulative probability
distribution
Transcribed Image Text:Let X be the number of applicants who apply for a senior level position at a large multinational corporation. The probability distribution of the random variable X is given in the following table. The outcomes (number of applicants) are mutually exclusive. Complete the table by calculating the cumulative probability distribution of X. Outcome (Number of applicants) 1 2 4 Probability distribution 0.35 0.30 0.12 0.15 0.08 Cumulative probability distribution
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