Give an example of each type, a near-monopoly and a regulated monopoly and suggest how their monopoly power affects the prices they charge and the service they provide.

(Typed out answers)

Please use a real-life example to explore the following:

When is Vega the highest –in the money, out of the money, or near the money? Why do you think this is the case?

NASA needs a solar sail for 3-unitCubeSat that will study near-Earth Asteroids. A CubeSat and Sails can weigh between 4-15-kg. This craft is supposed to sail for 2-3 years taking pictures of near-earth asteroids. It doesn’t need to travel fast but about 10-20 m/s. The sails are deployable for a set amount of time that you can arrange. The intensity of the sun is about 1.3 kW/m². You are to design the sail, you can choose the metallic coating, the shape of the sails and the dimensions to construct it. Recall that there is a reflection coefficient in radiation pressure, 1 for full absorption and 2 for full reflection.

1. You may have already noticed that the shape of your spectrophotometric titration curve is quite different than the shapes of the potentiometric titration curves which you have constructed in earlier experiments. Why? (HINT: Think about the mathematical relationships between pH and concentration and absorbance and concentration . . .).

2. When performing a potentiometric titration it is very important to make a large number of measurements near the endpoint, while for a spectrophotometric titration one actually ignores the measurements obtained near the endpoint. Explain.

3. In order for a photometric method to be used for a titration, what requirement(s) must be met by the titration system?

Answer the next 3 questions based on this information

Despite having a near-monopolistic market position, poor management and operational practices have left the Nautical Boating Network Corporation (NBN Co for short) significantly underfunded and needing to raise more capital. Because of its monopolistic position, debt holders are willing to lend NBN an additional $100 million, at a 1% premium to their current before-tax cost of debt of 4%. (This cost of debt of 5% will apply to all company debts after the debt raising). Even with its questionable management team, the additional debt is not expected to increase the risk of financial distress for NBN. The current market value of NBN’s assets are $300 million and the current Debt to Equity ratio is 0.5. NBN has a current cost of equity of 8% and is subject to a 30% corporate tax rate.

What is NBN’s current before-tax WACC?

6.67%

7.00%

6.00%

6.50%

If NBN’s unlevered cost of equity is currently 7.2%, what will be the new cost of equity after the debt raising?

12.00%

10.25%

11.00%

9.40%

If NBN’s cost of equity after the debt raising is 10%, what will be the after-tax WACC after the debt raising?

6.75%

5.81%

6.65%

6.17%

6.20%

DROP DATABASE class;CREATE DATABASE class;Use class;drop table if exists Class;drop table if exists Student;CREATE TABLE Class (CIN int PRIMARY KEY, FirstName varchar(255), LastName varchar(255), Gender varchar(1), EyeColor varchar(50), HairColor varchar(50), HeightInches int,CurrentGrade varchar(1));CREATE TABLE Student (SSN int PRIMARY KEY,FirstName varchar(255),LastName varchar(255), Age int,BirthMonth varchar(255),HeightInches int,Address varchar(255),City varchar(255),PhoneNumber varchar(12),Email varchar(255),FavColor varchar(255),FavNumber int);INSERT INTO Class VALUES(1, "David", "San", "M", "BRN", "BLK", 72, "-");INSERT INTO Class VALUES(2, "Jeff", "Gonzales", "M", "BRN", "BLK", 68, "B");INSERT INTO Class VALUES(3, "Anna", "Grayson", "F", "BRN", "BRN", 62, "A");INSERT INTO Class VALUES(4, "Kathryn", "Moloney", "F", "GRN", "BLK", 68, "B");INSERT INTO Class VALUES(5, "Randy", "Bernard", "M", "GRN", "BRN", 69, "A");INSERT INTO Class VALUES(6, "Andy", "Lam", "M", "BRN", "BLK", 59, "C");INSERT INTO Class VALUES(7, "Makoto", "Yuki", "F", "BRN", "BRN", 61, "A");INSERT INTO Class VALUES(8, "Pranil", "Watakana", "M", "BRN", "BLK", 63, "D");INSERT INTO Class VALUES(9, "Pierce", "Santos", "M", "BRN", "BLK", 74, "B");INSERT INTO Class VALUES(10, "Soliel", "Estrada", "F", "BRN", "BLU", 66, "B");INSERT INTO Class VALUES(11, "Jeff", "Bezos", "M", "BRN", "BRN", 65, "B");INSERT INTO Class VALUES(12, "Andy", "Chen", "M", "BRN", "BLK", 69, "A");INSERT INTO Class VALUES(13, "Makoto", "Amagi", "F", "BRN", "BRN", 64, "C");INSERT INTO Student VALUES(1, "David", "San", 22, "March", 72, "1234", "Flowerville", "231-246-4361", "[email protected]", "Blue", 7);INSERT INTO Student VALUES(2, "Randy", "Bernard", 21, "February", 69, "7123", "Rossette Park", "634-124-7452", "[email protected]", "Green", 12);INSERT INTO Student VALUES(3, "Andy", "Lam", 24, "December", 59, "9072", "Jefferson", "124-564-6354", "[email protected]", "Grey", 32);INSERT INTO Student VALUES(4, "Pranil", "Watakana", 23, "February", 63, "2146", "Rossette Park", "543-325-3521", "[email protected]", "Grey", 3);INSERT INTO Student VALUES(5, "Jeff", "Bezos", 22, "April", 65, "6312", "Grey Valley", "351-532-6439", "[email protected]", "Yellow", 0);INSERT INTO Student VALUES(6, "Makoto", "Amagi", 21, "September", 64, "39857", "Flowerville", "314-352-5321", "[email protected]", "Black", 3);

INSERT INTO Student VALUES(7, "Jeff", "Gonzales", 20, "October", 68, "4361", "Flowerville", "231-342-5467", "[email protected]", "Blue", 21);INSERT INTO Student VALUES(8, "Anna", "Grayson", 21, "January", 62, "6543", "Rossette Park", "634-423-5763", "[email protected]", "Green", 12);INSERT INTO Student VALUES(9, "Kathryn", "Moloney", 24, "May", 68, "5437", "Jefferson", "124-684-4131", "[email protected]", "Grey", 3);INSERT INTO Student VALUES(10, "Makoto", "Yuki", 19, "April", 61, "75632", "Rossette Park", "543-354-6421", "[email protected]", "Grey", 7);INSERT INTO Student VALUES(11, "Pierce", "Santos", 21, "January", 74, "3543", "GreyValley", "351-542-7541", "[email protected]", "Yellow", 10);INSERT INTO Student VALUES(12, "Soliel", "Estrada", 20, "June", 66, "3754", "Flowerville", "314-325-6543", "[email protected]", "Black", 5);INSERT INTO Student VALUES(13, "Andy", "Chen", 22, "September", 69, "3865", "Flowerville", "314-231-4233", "[email protected]", "Black", 3);

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1. Assign grades for students who lived in Rossette Park to be a B
2. Add another table for students who had an altered grade (in this case students who lived in Rossette Park)
3. Add the CIN, FirstName, LastName, Gender, Address, City, and Email of the students who had an altered grade to the table
4. Remove students from the student table who live in Rossette Park
5. Join all 3 tables and display the Names, Gender, and City of the students (Going to need to use ON)

Use
SET SQL_SAFE_UPDATES = 0;
To disable Safe Mode if prompted.

Not sure where to begin, as my instructor taught the theory but never any live coding session.

Is there a difference between the means of the total of rooms per hotel in Crete and Southern Aegean Islands? Answer your question by calculating an appropriate, symmetric, 95% confidence interval using a Z statistic and equal standard deviations in the two populations. Explain your findings.

REGION ID

1= Crete
2=Southern Aegean Islands
3=Ionian Islands

Is there a difference between the means of the total of rooms per hotel in Crete and Southern Aegean Islands? Answer your question by calculating an appropriate, symmetric, 95% confidence interval using a Z statistic and equal standard deviations in the two populations. Explain your findings

REGION ID

1= Crete
2=Southern Aegean Islands
3=Ionian Islands

The manager of a resort hotel stated that the mean guest bill for a weekend is $600 or less. A member of the hotel's accounting staff noticed that the total charges for guest bills have been increasing in recent months. The accountant will use a sample of future weekend guest bills to test the manager's claim.

(a)

Which form of the hypotheses should be used to test the manager's claim? Explain.

H₀: μ ≥ 600

H_a: μ < 600

H₀: μ ≤ 600

H_a: μ > 600

H₀: μ = 600

H_a: μ ≠ 600

A) The hypotheses H₀: μ ≥ 600 and H_a: μ < 600 should be used because the accountant wants to test the manager's claim that the mean guest bill μ is greater than or equal to 600 and find evidence to support μ < 600.

B)The hypotheses H₀: μ ≤ 600 and H_a: μ > 600 should be used because the accountant wants to test the manager's claim that the mean guest bill μ is less than or equal to 600 and find evidence to support μ > 600.   

C)The hypotheses H₀: μ = 600 and H_a: μ ≠ 600 should be used because the accountant wants to test the manager's claim that the mean guest bill μ is equal to 600 and find evidence to support μ ≠ 600.

(b)

What conclusion is appropriate when

H₀

cannot be rejected?

A)We are able to conclude that the manager's claim is wrong. We can conclude that μ = 600.

B)We are not able to conclude that the manager's claim is wrong.We cannot conclude that μ > 600.    

C) We are not able to conclude that the manager's claim is wrong. We cannot conclude that μ ≠ 600.

D) We are able to conclude that the manager's claim is wrong. We can conclude that μ ≤ 600.

E) We are not able to conclude that the manager's claim is wrong. We can conclude that μ ≥ 600.

(c)

What conclusion is appropriate when

H₀

can be rejected?

A) We are not able to conclude that the manager's claim is wrong. We can conclude that μ < 600.

B) We are not able to conclude that the manager's claim is wrong. We can conclude that μ > 600.    

C) We are able to conclude that the manager's claim is wrong. We can conclude that μ < 600.

D)We are able to conclude that the manager's claim is wrong. We can conclude that μ ≠ 600.

E) We are able to conclude that the manager's claim is wrong. We can conclude that μ > 600.