Coursehelp
May 23, 2019

## (Solved) : Use R Show Code Problem 2 Inspector Receives Batch Widgets Used Manufacture New Product Ba Q28095270 . . .

Use R and SHOW ALL CODE

## Problem 2
*An inspector receives a batch of widgets which will be used tomanufacture a new product. The batch will be rejected and sent backto the manufacturer if the proportion of defective widgets in thebatch exceeds 10%. The inspector selected thirty widgets from thebatch and tested them. He found that of the thirty widgets hetested, two were defective.*

*Let the proportion of defective widgets in the batch, \$p in{0.1, 0.2, 0.3, …, 0.9}\$, and assign a uniform prior to \$p\$(that is, according to the prior distribution, \$P(p = p_i) = P(p =p_j)\$ for every \$p_i, p_j in {0.1, 0.2, 0.3, …, 0.9}\$; inwords, each \$p\$ is equally likely). Given the results of theinspection:*

*Compute the maximum* a-posteriori *(MAP) estimator for \$p\$ anda (approximately) 95% credible interval for \$p\$ using the posteriordistribution.*

. . .

OR
OR

## Questions viewed by other students

• QUESTION : (Solved) : Use Questions 1 4 Reality Patients Needing Organ Transplant Organs Available National Wait Q31281311 . . .

Use for Questions 1 to 4 In reality, there are more patients needing an organ transplant than there are organs available. Because of this, a national waiting list is used to orderly assign available organs to patients. Suppose a researcher records monthly the number of patients that die on the waiting list, are transplanted, or are censored (due to the end of the study). Assume that the censoring occurs exactly at the end of each month. Let D be the month that the subject would have died on the waiting list without a transplant. Let T be the month the patient would have been transplanted had they remained on the waiting list. Finally, let C be the end of the month a random patient is censored from the study. The challenge is that once a patient is censored or is transplanted we do not know the time at which they would have died on the waiting list. That is, we only get to observe min(D.T.C) and the type of event (death, transplantation, censoring). Assume throughout that P(T = tlT > t-land D > t _ 1 and C > t-1-P(T = t|T > t-land D > t-1) and P(D = tlT > t _ 1 and D > t _ 1 and C > t-1)-P(D = t[T > t-1and D > t-1) Suppose that the data were collected over the first 6 months of being on the waiting list for 1,000 patients. So Dt the number of people that die off the waiting list, · Tt = the number transplanted during a particular month, . Cr the number of people censored at the end of the month, and ne the number of people at risk during the start of the interval. Ct nt 1 50 20 50 1000 Show transcribed image text Use for Questions 1 to 4 In reality, there are more patients needing an organ transplant than there are organs available. Because of this, a national waiting list is used to orderly assign available organs to patients. Suppose a researcher records monthly the number of patients that die on the waiting list, are transplanted, or are censored (due to the end of the study). Assume that the censoring occurs exactly at the end of each month. Let D be the month that the subject would have died on the waiting list without a transplant. Let T be the month the patient would have been transplanted had they remained on the waiting list. Finally, let C be the end of the month a random patient is censored from the study. The challenge is that once a patient is censored or is transplanted we do not know the time at which they would have died on the waiting list. That is, we only get to observe min(D.T.C) and the type of event (death, transplantation, censoring). Assume throughout that P(T = tlT > t-land D > t _ 1 and C > t-1-P(T = t|T > t-land D > t-1) and P(D = tlT > t _ 1 and D > t _ 1 and C > t-1)-P(D = t[T > t-1and D > t-1) Suppose that the data were collected over the first 6 months of being on the waiting list for 1,000 patients. So Dt the number of people that die off the waiting list, · Tt = the number transplanted during a particular month, . Cr the number of people censored at the end of the month, and ne the number of people at risk during the start of the interval. Ct nt 1 50 20 50 1000

. . .

• QUESTION : (Solved) : Use Quotation Marks Html Thought N Would Work Q32812733 . . .

How can i use quotation marks in html. I thought ” and n wouldwork but it did not.

<textarea placeholder=’ “I think, therefore I drink.”-Homerism #73 n n Or, supply your own wisdom here, if you thinkyou’re so smart’ rows=”4″ cols=”70″></textarea>

also I need to write ” <References available upon request>” and that company logo. the c inside the O.

“I think, therefore I drink.” – Home rism #73 Or,supply your own wisdom here, if you think you’re so smart Show transcribed image text “I think, therefore I drink.” – Home rism #73 Or,supply your own wisdom here, if you think you’re so smart

. . .