Consider the following sample of observations on coating thickness for low-viscosity paint.

0.86 0.88 0.88 1.07 1.09 1.17 1.29 1.31
1.46 1.49 1.59 1.62 1.65 1.71 1.76 1.83

Assume that the distribution of coating thickness is normal (a normal probability plot strongly supports this assumption).

a. Calculate a point estimate of the mean value of coating thickness. (Round your answer to four decimal places.)
b. Calculate a point estimate of the median of the coating thickness distribution. (Round your answer to four decimal places.)

State which estimator you used and which estimator you might have used instead. (Select all that apply.)
c. Calculate a point estimate of the value that separates the largest 10% of all values in the thickness distribution from the remaining 90%. [Hint: Express what you are trying to estimate in terms of μ and σ.] (Round your answer to four decimal places.)
d. Estimate P(X < 1.6), i. e., the proportion of all thickness values less than 1.6. [Hint: If you knew the values of μ and σ, you could calculate this probability. These values are not available, but they can be estimated.] (Round your answer to four decimal places.)
e. What is the estimated standard error of the estimator that you used in part (b)? (Round your answer to four decimal places.)

a)

And for this case if we use this formula we got:

b) Since we have n =16 values for the sample the median can be calculated as the average between position 8th anf 9th and we got:

c)   (a)

  (b)

Both conditions are equivalent on this case. We can use the z score again in order to find the value a.  

As we can see on the figure attached the z value that satisfy the condition with 0.9 of the area on the left and 0.1 of the area on the right it's z=1.28. On this case P(Z<1.28)=0.9 and P(z>1.28)=0.1

If we use condition (b) from previous we have this:

But we know which value of z satisfy the previous equation so then we can do this:

And if we solve for a we got

So the value of height that separates the bottom 90% of data from the top 10% is 1.8024.  

d)

The variance for this estimator is given by:

We can assume the obervations independent so then we have:

And replacing we got:

And the standard error would be given by:

Step-by-step explanation:

Data given:

0.86 0.88 0.88 1.07 1.09 1.17 1.29 1.31  1.46 1.49 1.59 1.62 1.65 1.71 1.76 1.83

Part a

We can calculate the mean with the following formula:

And for this case if we use this formula we got:

Part b

For this case in order to calculate the median we need to put the data on increasing way like this:

0.86 0.88 0.88 1.07 1.09 1.17 1.29 1.31 1.46 1.49  1.59 1.62 1.65 1.71 1.76 1.83

Since we have n =16 values for the sample the median can be calculated as the average between position 8th anf 9th and we got:

Part c

For this case we can assume that the mean is

And we can calculate the population deviation with the following formula:

And if we replace we got:  

And assuming normal distribution we have this:

For this part we want to find a value a, such that we satisfy this condition:

  (a)

  (b)

Both conditions are equivalent on this case. We can use the z score again in order to find the value a.  

As we can see on the figure attached the z value that satisfy the condition with 0.9 of the area on the left and 0.1 of the area on the right it's z=1.28. On this case P(Z<1.28)=0.9 and P(z>1.28)=0.1

If we use condition (b) from previous we have this:

But we know which value of z satisfy the previous equation so then we can do this:

And if we solve for a we got

So the value of height that separates the bottom 90% of data from the top 10% is 1.8024.  

Part d

The median is defined as :

The variance for this estimator is given by:

We can assume the obervations independent so then we have:

And replacing we got:

And the standard error would be given by:

20days louise can read300 pg

step-by-step explanation:

1 day 15 pg 300: 15= 20

x day 300 pg