The table below contains average cost data for four different-sized plants—1, 2, 3, and 4—which are the only four sizes possible.
   

a. What is the economic capacity output for each of the four plants?

     Plant 1:
     Plant 2:
     Plant 3:
     Plant 4:

b. In what plant and at what output is minimum long-run average cost achieved?

    Output of  in  (Click to select)  1  2  3  4  .

c. Which plant is the right size to produce an output of 210?

       (Click to select)  Plant 1  Plant 2  Plant 3  Plant 4

d. Which plant is the right size to produce an output of 110?

       (Click to select)  Plant 1  Plant 2  Plant 3  Plant 4

1. Economics assumes people are interested in their rational self interest. Suppose a person works 20 years. Which job is the better choice (ceteris paribus)? An explanation of what ceteris paribus means should be given before deciding which job is better.

2. If the person invested their extra earnings each year how much additional money would they have in their account at the end of 20 years when they retire? The interest rate is constant at 10 percent per year.

3. Suppose the person works 40 years. Which job is the better choice? I will assume that the person has reached the top of the pay scale and pay will receive the same yearly salary they received in their 20th year of work for the remaining years they work.

Consider the following time series data.

a. Use α = 0.2 to compute the exponential smoothing forecasts for the time series.

Compute MSE. (Round your answer to two decimal places.)

MSE =  

b. Use a smoothing constant of α = 0.4 to compute the exponential smoothing forecasts.

Does a smoothing constant of 0.2 or 0.4 appear to provide more accurate forecasts based on MSE? Explain.

The exponential smoothing using α = 0.4 provides a better forecast since it has a larger MSE than the exponential smoothing using α = 0.2.

The exponential smoothing using α = 0.2 provides a better forecast since it has a larger MSE than the exponential smoothing using α = 0.4.    

The exponential smoothing using α = 0.4 provides a better forecast since it has a smaller MSE than the exponential smoothing using α = 0.2.

The exponential smoothing using α = 0.2 provides a better forecast since it has a smaller MSE than the exponential smoothing using α = 0.4.

Explain the following terms

1. Native structure

2. Allosteric enzyme

3. Transition state

4. Triacyglycerol

5. Induced fit

6. Catalytic site

PROBLEM 5. A box contains 10 tickets labeled 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Draw four tickets and find the probability that the largest number drawn is 8 if:

(a) the draws are made with replacement.

(b) the draws are made without replacement.

PROBLEM 6. Suppose a bakery mixes up a batch of cookie dough for 1,000 cookies. If there are raisins in the dough, it's reasonable to assume raisins will independently have a .001 chance of ending up in any particular cookie (assuming that raisins don't clump together), so that the number of raisins in a cookie behaves like a binomial (n= # raisins in whole batch, p= .001 ) random variable.

(a). How many raisins should be put into a batch of dough to make the chance of a cookie containing at least one raisin very close to 99% ? Calculate this using the binomial distribution.

(b). If there is a 99% probability that a cookie contains at least one raisin, what is the probability that a cookie contains exactly one raisin? Exactly 4 raisins? Calculate these using the binomial distribution, with the n you found in part (a).

(c). Now assume that the number of raisins in a cookie is Poisson, with mean mu. What is the right value of mu that makes the probability of at least one raisin in a cookie equal to 99% ?

(d). Assuming that the number of raisins in a cookie is Poisson with the mu that you found in part (c), what is the probability that a cookie contains exactly one raisin? Exactly 4 raisins?

REMARK There's something a bit subtle going on in this cookie story. For example, if you put 2,000 raisins into the dough and make 1,000 cookies, with raisins independently equally likely to end up in any of the 1,000 cookies, then of course the sum over all 1,000 cookies of the raisins-per-cookie numbers will add up to exactly 2,000. However, the fractions of cookies with exactly k raisins will, with very high probability, be close to Poisson (mu=2) probabilities, so those fractions would behave as though the numbers of raisins in cookies were independent Poisson (mu=2) random variables. If the numbers of raisins per cookie really were independent Poisson (mu=2) random variables, then the total number of raisins would be Poisson with mean 2,000 (which is approximately normal, with mean 2,000 and standard deviation SQRT(2,000) = 44.72). It turns out that the exact joint behavior of the rasins per cookie numbers with 2,000 raisins total is the same as the joint behavior with independent Poisson (mu=2) raisins per cookie, conditional on the total number of raisins being exactly 2,000.

Consider the following time series data. t 1 2 3 4 5 6 7 yt 10 9 7 8 6 4 4 a. Construct a time series plot. What type of pattern exists in the data? b. Develop the linear trend equation for this time series. c. What is the forecast for t=8?

Write the Lewis Structure and Shape 1. TeF4

2. ClF3

3.XeF2

4. I3^-

5. NO^+

6.CF4

7.PCl4^+

8.XeF4

Q.1. A population consists of five numbers 2, 4, 5, 8, 12. (i) List all possible samples of size 2 that can be drawn from this population with replacement. (ii) Construct the sampling distribution of the samples mean drawn in part (i). (iii) Verify that mean of the sampling distribution is equal to the population mean and δ = δ/ .

solve the above problem step by step in proper format

Consider the following time series.

b. Develop the quadratic trend equation for the time series. Enter negative value as negative number.(to 3 decimals)

T_t = ______ + _______t + ________ t²

c. What is the forecast for t = 8?