# Question 1(a) Since X and Y be independent random variables both with the same mean µ 0.So E(X)=µ and E(Y)=µNow W=aX + bY a,b are constantsi. E(W)=E(aX + bY)E(W)=E(aX)+E(bY) ...

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Question 2c ii iii

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Question 1 (a) Since X and Y be independent random variables both with the same mean µ 0. So E(X)=µ and E(Y)=µ Now W=aX + bY a,b are constants i. E(W)=E(aX + bY) E(W)=E(aX)+E(bY) Linearity Property E(W)=aE(X)+bE(Y) because E(cX)=cE(X) E(W)=a*µ +b*µ E(W)=(a+b)*µ ii. W is unbiased estimator of µ if E(W)=µ · E(W)=(a+b)*µ =µ · (a+b)*µ =µ · a+b=1 · b=1-a So W=a*X+b*Y=a*X+(1-a)*Y W=a*X+(1-a)*Y So W is unbiased estimator of µ (b) i. We know that value of Probability must lie between 0 and 1 So Now So Multiply both sides by 4 Now But So Thus ii. Now we know that Likelihood function of from given data Now Let C== Constant iii. iv. For Maximum Likelihood So · · So or or But We Know that So only value fulfills this condtions Now for Fair unbiased die Since so die is biased with higher probability of outcomes of 1 and 6 (C) i. Let ‘X’ follows Geometric distribution with parameter p. So The Probability mass function is Now Likelihood function is Differentiate w.r.t P For Maximum Likelihood Now So ii. Now we know that Question 2 (a) Given data The sample mean windscreen replacement time , 23515 Sample standard deviation windscreen replacement times was 5168 hours of flight i.e s=5168 n=84 i. We will use t-test to find Confidence interval Now Degree of Freedom dof=n-1=84-1=83 From t-table t at 90% with 83 dof= Now 90% confidence interval, CI CI= CI=[22577.05, 24452.95] ii. Interpretation:- We are 90% confident that the mean replacement time of this type aircraft windscreen lies in in between 22577.05 to 24452.95 hours of flight. (b) i. We have Now Standard Error, SE= Level of significance, Critical value, z=1.96 So Required Confidence interval CI= ( CI=[-0.0141 , -0.0021] Hence required 95% confidence interval is [-0.0141,-0.0021] ii. Confidence interval does not contain zero so there is no evidence to conclude that proportions of conventionally treated hypertension patients who later suffered a stroke and the proportion of hypertension patients treated with the alternative drug who later suffered a stroke. So there is no evidence to conclude that the proportions of hypertension patients who suffered a stroke would not depend on which treatment they were being given. The data is not consistent with that suggestion. (c) i. Perform Descriptive analysis on Coin 1 and coin 4 using Descriptive Statistics Tool in Minitab. We get As we can see from above data standard deviation in Coin 1 is more than that of standard deviation in Coin 4.This means that Coin 1 has less Variability that of coin 4. ii. Now we have to obtain 90% two sample t-interval using minitab. We will use “2 sample t” tool from basics statistics. Then Select “Each sample is in its own column” option and Select “Coin 1” and “Coin 4” coloumn . Then set parameter like confidence interval 90%.Assume Equal Variances. Then click on OK. We will get following output So at 90% Confidence Interval true population means lies between interval (0.709,1.1551). iii. The distribution used in constructing the confidence interval is T distribution (Two tailed).The value of its parameter is the difference i.e, 1.13 from the output. The t-value is a way to quantify the difference between the population means. iv. Do the process same as part two but select coin4 in sample 1 and coin 1 in sample 2.We will get following Output. The sign of a t-value tells us the direction of the difference in sample mean. Negative t value means that it lies to the left of the mean.
Answered 1 days AfterFeb 25, 2023

## Answer To: Question 1(a) Since X and Y be independent random variables both with the same mean µ 0.So...

Baljit answered on Feb 27 2023
Question 1
(a) Since X and Y be independent random variables both with the same mean µ 0.
So
E(X)=µ and E(Y)=µ
Now
W=aX + bY a,b are constants
i. E(W)=E(aX + bY)
E(W)=E(aX)+E(bY) Linearity Proper
ty
E(W)=aE(X)+bE(Y) because E(cX)=cE(X)
E(W)=a*µ +b*µ
E(W)=(a+b)*µ
ii.
W is unbiased estimator of µ if
E(W)=µ
· E(W)=(a+b)*µ =µ
· (a+b)*µ =µ
· a+b=1
· b=1-a
So
W=a*X+b*Y=a*X+(1-a)*Y
W=a*X+(1-a)*Y
So W is unbiased estimator of µ
(b)
i. We know that value of Probability must lie between 0 and 1
So
Now
So
Multiply both sides by 4
Now
But
So
Thus
ii.
Now we know that Likelihood function of from given data

Now Let C== Constant
iii.

iv. For Maximum Likelihood
So
·
· So or or
But We Know that
So only value fulfills this condtions
Now for Fair unbiased die
Since so die is biased with higher probability of outcomes of 1 and 6
(C)
i. Let ‘X’ follows Geometric distribution with parameter p.
So
The Probability mass function is
Now Likelihood function is
Differentiate w.r.t P
For Maximum Likelihood
Now
So
ii. Now we know that
Question 2
(a)
Given data
The sample mean windscreen replacement time , 23515
Sample standard deviation windscreen replacement times was 5168 hours of flight i.e s=5168
n=84
i.
We will use t-test to find Confidence interval
Now Degree of Freedom dof=n-1=84-1=83
From t-table
t at 90% with 83 dof=
Now 90% confidence interval, CI
CI=
CI=[22577.05, 24452.95]
ii. Interpretation:- We are 90% confident that the mean replacement time of this type aircraft windscreen lies in in between 22577.05 to 24452.95 hours of flight.
(b)
i.
We have

Now

Standard Error, SE=
Level of significance,
Critical value, z=1.96
So Required Confidence interval
CI= (
CI=[-0.0141 , -0.0021]
Hence required 95% confidence interval is...
SOLUTION.PDF

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