4. bayesian estimation bookmark this page setup: in this problem, we will explore the intersection of bayesian and frequentist inference. let , , ⋯ , ∼i. i.d ,theta) , for some unknown positive number theta , which is our parameter of interest. suppose that we are unable to come up with a prior distribution for theta . (a) 1 point possible (graded) compute the maximum likelihood estimator of theta . you may use the variables , ∑= , and ∑= . (enter sigma_i(x_i) for ∑= and sigma_i(x_i^2) for ∑= . do not worry if the parser does not render properly; the grader works independently. if you wish to have proper rendering, enclose sigma_i(x_i) by brackets. )

step-by-step explanation:

a.   since the profit made in 2012 is equal to the profit made in 2014, the profit is           symmetric about the line y = 120,000.

step-by-step explanation:

given that megan owns a clothing boutique.

the profits of 2010, 11, are recorded and shown as follows:

years since     2010 0     1     2     3       4       5

profits made in 1000's   39       84     111     120   111     84

we find that profit gradually increases form 2010 to 2013 upto 120,   then starts decreasing.

the profits for 2012 and 2014 are the same while that of 2011 and 2015 are also the same.

this shows that there is symmetry about the line profit = 120000

hence answer is:

a.   since the profit made in 2012 is equal to the profit made in 2014, the profit is           symmetric about the line y = 120,000.

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the answer would be 150 books

step-by-step explanation:

if you divide 300 by 2 you will get 150

1. 3. can also be expressed as (22/7)

2. -4.5

3. 3.6

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