Is Your Car “Made in the U.S.A.”? The phrase “made in the U.S.A.” has become a familiar battle cry as U.S. workers try to protect their jobs from overseas competition. For the past few decades, a ma- jor trade imbalance in the United States has been caused by a flood of imported goods that enter the country and are sold at lower cost than comparable American-made goods. One prime concern is the automotive industry, in which the number of imported cars steadily increased during the 1970s and 1980s. The U.S. automobile industry has been besieged with complaints about product quality, worker layoffs, and high prices, and has spent billions in advertising and research to produce an American-made car that will satisfy consumer demands. Have they been successful in stopping the flood of imported cars purchased by American consumers? The data in the table represent the numbers of imported cars y sold in the United States (in millions) for the years 1969–2009. To simplify the analysis, we have coded the year using the coded variable x = Year - 1969.

1. Using a scatterplot, plot the data for the years 1969–1988. Does there appear to be a linear relationship between the number of imported cars and the year?

2. Use a computer software package to find the least-squares line for predicting the number of imported cars as a function of year for the years 1969–1988.

3. Is there a significant linear relationship between the number of imported cars and the year?

4. Use the computer program to predict the number of cars that will be imported us- ing 95% prediction intervals for each of the years 2007, 2008, and 2009.

5. Now look at the actual data points for the years 2007–2009. Do the predictions obtained in step 4 provide accurate estimates of the actual values observed in these years? Explain.

6. Add the data for 1989–2009 to your database, and recalculate the regression line. What effect have the new data points had on the slope? What is the effect on SSE?

7. Given the form of the scatterplot for the years 1969–2009, does it appear that a straight line provides an accurate model for the data? What other type of model might be more appropriate? (Use residual plots to help answer this question.)

1. List the owner's name of all male customers in the bank who have a ’Checking’ account.
2. Find all accounts associated with ’Alexander Felix’.
3. For each account of the previous question, compute the Balance, and return a table that shows the account number, type, and balance for each account (hint: use UNION).
4. The list of customer names that have transactions greater than or equal to one thousand dollars.

Bank.sql is under this statement.

DROP DATABASE IF EXISTS Bank;
CREATE DATABASE Bank;
USE Bank;

DROP TABLE IF EXISTS transaction;
DROP TABLE IF EXISTS customer;
DROP TABLE IF EXISTS account;

CREATE TABLE customer (
name VARCHAR(20),
sex CHAR(1),
ssn CHAR(9) NOT NULL,
phone CHAR(15),
dob DATE,
address VARCHAR(50),
PRIMARY KEY(ssn)

);
  
CREATE TABLE account (
number CHAR(16) UNIQUE NOT NULL,
open_date DATE,
type CHAR(20),
owner_ssn CHAR(9) NOT NULL,
PRIMARY KEY(number)
);
  
CREATE TABLE transaction (
id INT(20) UNIQUE NOT NULL,
amount DECIMAL(9,2),
tdate DATE,
type CHAR(10),
account_num CHAR(16),
PRIMARY KEY(id)
);

INSERT INTO customer VALUE ('John Adam', 'M', '512432341', '(438) 321-2553', '1987-11-15',NULL);
INSERT INTO customer VALUE ('Alexander Felix', 'M', '724432341', '(541) 321-8553', '1991-05-22', NULL);
INSERT INTO customer VALUE ('Andrew William', 'M', '861894272', '(308) 692-1110', '1995-01-04', NULL);
INSERT INTO customer VALUE ('Ana Bert', 'F', '844192241', '(203) 932-7000', '1982-12-07', '23 Boston Post Rd, West Haven, CT 06516');

INSERT INTO account VALUE ('1111222233331441', '2018-12-03', 'Checking', '861894272');
INSERT INTO account VALUE ('2111222233332442', '2019-01-06', 'Saving', '512432341');
INSERT INTO account VALUE ('3111222233333443', '2017-09-22', 'Checking', '844192241');
INSERT INTO account VALUE ('4111222233335444', '2016-04-11', 'Checking', '724432341');
INSERT INTO account VALUE ('5111222233339445', '2018-11-05', 'Saving', '724432341');
INSERT INTO transaction VALUE (1001, 202.50, '2019-08-15', 'Deposit', '5111222233339445');
INSERT INTO transaction VALUE (1002, 100.00, '2019-09-21', 'Deposit','2111222233332442');
INSERT INTO transaction VALUE (1003, 200.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1004, 50.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1005, 1000.00, '2019-09-29', 'Deposit','3111222233333443');
INSERT INTO transaction VALUE (1006, -202.50, '2019-08-29', 'Withdraw', '5111222233339445');
INSERT INTO transaction VALUE (1007, 50.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1008, 50.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1009, -10.00, '2019-09-26', 'Withdraw', '2111222233332442');
INSERT INTO transaction VALUE (1010, 50.00, '2019-09-29', 'Deposit', '4111222233335444');
INSERT INTO transaction VALUE (1011, 320.00, '2019-09-29', 'Deposit', '5111222233339445');
INSERT INTO transaction VALUE (1012, 50.00, '2019-09-18', 'Deposit', '4111222233335444');
INSERT INTO transaction VALUE (1013, 5000.00, '2019-06-21', 'Deposit', '1111222233331441');
INSERT INTO transaction VALUE (1014, -100.00, '2019-09-02', 'Withdraw', '1111222233331441');
INSERT INTO transaction VALUE (1015, -200.00, '2019-09-08', 'Withdraw', '1111222233331441');

Alice J. and Bruce M. Smith are married taxpayers who file a joint return. Their social security numbers are 123-45-6789 and 111-11-1111, respectively. Alice’s birthday is September 21, 1966, and Bruce’s is June 27, 1965. They live at 473 Revere Avenue, Lowell, MA 01850. Alice is the office manager for Lowell Dental Clinic. Bruce is the self-employed physical therapist.

The following information is shown on Alice’s Wage and Tax Statement (Form W-2) for 2017.

During 2017, Bruce recorded the following items of his business:

Bruce made the quarterly federal tax payments totaled $40,000  (same as tax withheld and should be reported on line 64)

The Smiths provide over half of the support of their two children, Cynthia (born January 25, 1989, Social security number 123-45-6788) and John (born February 7, 1995, Social Security number 123-45-6786). Both children are full-time students and live with the Smiths except when they are away at college. Cynthia earned $4,200 from a summer internship in 2017, and John earned $3,800 from a part-time job.

During 2017, The Smiths furnished 60% of the total support of Bruce’s widower father, Sam Smith (born March 6, 1937, social security number 123-45-6777). Sam died in November, and Bruce, the beneficiary of a policy on Sam’s life, received life insurance proceeds of $800, 000 on December 28.

The Smiths also made some investment activities during 2017. The following information is shown their investment income / (loss) for 2017.

The Smiths had the following expenses relating to their personal residence during 2017

Prepare the Federal income tax return of 2017 for the Smiths. You will include Form 1040, Schedule C, Schedule SE (use the first page of SE to calculate), and Schedule A.

1. Find the total amount of ’Deposit’ transactions at the bank
2. Find the list of transactions (statement) of September 2019 (09/01/2019 to 09/30/2019) for account ’1111222233331441’ (note: look at the date format).
3. Find the balance of ’1111222233331441’ before 09/01/2019 ((not including 09/01/2019).
4. Find the name of the customer that deposited the highest amount with one transaction (include the transaction amount).

The SQL code to solve these problems is below:

DROP DATABASE IF EXISTS Bank;
CREATE DATABASE Bank;
USE Bank;

DROP TABLE IF EXISTS transaction;
DROP TABLE IF EXISTS customer;
DROP TABLE IF EXISTS account;

CREATE TABLE customer (
name VARCHAR(20),
sex CHAR(1),
ssn CHAR(9) NOT NULL,
phone CHAR(15),
dob DATE,
address VARCHAR(50),
PRIMARY KEY(ssn)

);
  
CREATE TABLE account (
number CHAR(16) UNIQUE NOT NULL,
open_date DATE,
type CHAR(20),
owner_ssn CHAR(9) NOT NULL,
PRIMARY KEY(number)
);
  
CREATE TABLE transaction (
id INT(20) UNIQUE NOT NULL,
amount DECIMAL(9,2),
tdate DATE,
type CHAR(10),
account_num CHAR(16),
PRIMARY KEY(id)
);

INSERT INTO customer VALUE ('John Adam', 'M', '512432341', '(438) 321-2553', '1987-11-15',NULL);
INSERT INTO customer VALUE ('Alexander Felix', 'M', '724432341', '(541) 321-8553', '1991-05-22', NULL);
INSERT INTO customer VALUE ('Andrew William', 'M', '861894272', '(308) 692-1110', '1995-01-04', NULL);
INSERT INTO customer VALUE ('Ana Bert', 'F', '844192241', '(203) 932-7000', '1982-12-07', '23 Boston Post Rd, West Haven, CT 06516');

INSERT INTO account VALUE ('1111222233331441', '2018-12-03', 'Checking', '861894272');
INSERT INTO account VALUE ('2111222233332442', '2019-01-06', 'Saving', '512432341');
INSERT INTO account VALUE ('3111222233333443', '2017-09-22', 'Checking', '844192241');
INSERT INTO account VALUE ('4111222233335444', '2016-04-11', 'Checking', '724432341');
INSERT INTO account VALUE ('5111222233339445', '2018-11-05', 'Saving', '724432341');
INSERT INTO transaction VALUE (1001, 202.50, '2019-08-15', 'Deposit', '5111222233339445');
INSERT INTO transaction VALUE (1002, 100.00, '2019-09-21', 'Deposit','2111222233332442');
INSERT INTO transaction VALUE (1003, 200.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1004, 50.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1005, 1000.00, '2019-09-29', 'Deposit','3111222233333443');
INSERT INTO transaction VALUE (1006, -202.50, '2019-08-29', 'Withdraw', '5111222233339445');
INSERT INTO transaction VALUE (1007, 50.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1008, 50.00, '2019-09-29', 'Deposit', '2111222233332442');
INSERT INTO transaction VALUE (1009, -10.00, '2019-09-26', 'Withdraw', '2111222233332442');
INSERT INTO transaction VALUE (1010, 50.00, '2019-09-29', 'Deposit', '4111222233335444');
INSERT INTO transaction VALUE (1011, 320.00, '2019-09-29', 'Deposit', '5111222233339445');
INSERT INTO transaction VALUE (1012, 50.00, '2019-09-18', 'Deposit', '4111222233335444');
INSERT INTO transaction VALUE (1013, 5000.00, '2019-06-21', 'Deposit', '1111222233331441');
INSERT INTO transaction VALUE (1014, -100.00, '2019-09-02', 'Withdraw', '1111222233331441');
INSERT INTO transaction VALUE (1015, -200.00, '2019-09-08', 'Withdraw', '1111222233331441');

The table to the right lists the average number of hours worked in a week and the average weekly earnings for U.S. production workers from 1967 to 1996. (The World Almanac 1998)

1) Construct a scatter diagram and comment on the relationship, if any, between the variables Weekly Hours and Weekly Earnings.

2) Determine and interpret the correlation for hours worked and earnings. Based upon the value of the correlation, is your answer to the previous question reasonable?

3) Based upon the data given, estimate the average weekly earnings for a workweek of 33.8 hours. How confident are you in your estimate? You should use a linear regression model to make your prediction. To create the linear regression model in Excel, right-click on a data point and click Add Trendline... In the options that display on the right, click Display Equation on chart.

4) Increase/decrease in weekly hours: a) For a production worker who wishes to increase weekly earnings, would you recommend a decrease in hours worked per week? Why or why not? b) Does a decrease in hours worked cause an increase in weekly pay? c) What other variables could contribute to an increase in weekly pay?

Part 2 1) Construct a scatter diagram and comment on the relationship, if any, between the variables Year and Hours Worked.

2) Determine and interpret the correlation for the year and hours worked. Based upon the value of the correlation, is your answer to the previous question reasonable?

3) Based upon the data given, estimate the average weekly hours worked this year. How confident are you in your estimate? You should use a linear regression model to make your prediction.

4) Assuming a linear correlation between these two variables, what will happen to the average weekly hours worked in the future? Is it possible for this pattern to continue indefinitely? Explain.

part 3

1) Construct a scatter diagram and comment on the relationship, if any, between the variables Year and Weekly Earnings.

2) Determine and interpret the correlation for the year and weekly earnings. Based upon the value of the correlation, is your answer to the previous question reasonable?

3) Based upon the data given, estimate the average weekly earnings this year. How confident are you in your estimate? You should use a linear regression model to make your prediction.

4) Assuming a linear correlation between these two variables, what will happen to the average weekly earnings in the future? Is it possible for this pattern to continue indefinitely? Explain.

data:

Part 2: Schedule M1 (CT1) and M2 (CT2) For Rocky Mountain Equipment Corporation Form 1120-F

The Rocky Mountain Equipment Corporation, a Colorado Corporation, was formed by two Colorado State University business school graduates. The Rocky Mountain Equipment Corporation incorporated on October 20, 1974. The main line of business is selling recreational equipment to outdoor enthusiasts. Starting in their parents’ garage, they have grown the corporation to a multimillion dollar business.

To comply with accounting requirements, the company uses an accrual method of accounting. Its accumulated earnings and profits as of December 31, 2016, were $1,200. It made cash distributions during its 2016 calendar tax year of $140,089. This consisted of $85,089 to preferred shareholders and $55,000 to common shareholders. The entire distribution to preferred shareholders is a taxable dividend. The $27,500 distribution on March 15, 2016, to common shareholders is a taxable dividend to extent of $27,318 (99.33%), and the $27,500 distribution on September 15, 2016, to common shareholders is a taxable dividend to the extent of $26,118 (94.97%).

The following profit and loss account appeared in the books of the Rocky Mountain Equipment Corporation for calendar year 2016. It is required to file Form 1120 and completes Form 1120-F (M-1 and M-2).

Please prepare Schedule M-1 for Rocky Mountain Equipment Corporation using the financial information and the Form 1120 line items provided above.

Please prepare Schedule M-2 for Rocky Mountain Equipment Corporation using the retained earning information provided. To accurately calculate and support the ending balance, please complete a Retained Earnings Reconciliation Table.

Background: The Clearfield Cheese Company was established by two brothers, Terry and Ted Edwards, in 1931, in Clearfield, Pennsylvania. This section of Central Pennsylvania's economy was based largely upon coal and agriculture at this point in time. The U.S. economy was in the throes of what is usually referred to as the Great Depression, and coal production and agriculture were both experiencing the effects of the slumping economy. The farms in the area were mostly small- to medium-size dairy operations. The farmers were under financial duress because they could not sell their milk in the local area for a price to cover their cost of production. There were better market opportunities in Pittsburgh and Harrisburg, Pennsylvania, but their transportation costs put their "landed cost" at a disadvantage with dairy farmers in Erie, Pennsylvania, and Eastern Ohio. The Edwards brothers were not farmers but rather entrepreneurs and owned several tanker trucks, which could be used for hauling milk. They decided that instead of using their equipment to haul milk to potential markets for very meager profits they would start a cheese processing operation in Clearfield. They had some savings and were able to borrow money from The First National Bank of Clearfield, which was still solvent. Their grandfather who had emigrated from Switzerland was knowledgeable about cheese production and processing and helped them get started. They purchased milk from local farmers with lenient payment terms and started a successful venture. World War II presented some challenges in terms of labor supply and fuel rationing, but they survived and prospered by hiring more women and utilizing more rail service. The next major hurdle was the government-subsidized cheese producers in Canada selling into the Pennsylvania market in the 1980s. Tom Powers, CEO of the Clearfield Cheese Company, with the assistance of two of his key executives, Andy Reisinger (CIO) and Sandy Knight (CSCO), developed a plan, which included improving their supply chain operation efficiency by lowering inventory levels with better forecasting and procurement practices. They expanded their product offerings by adding cottage cheese, sour cream, and yogurt. They also purchased a Canadian company in 1995 because their Canadian sales were growing. This lowered their costs to serve the growing Canadian market and made them much more competitive in Canada. This was an important step to make them a global company.

Current Situation Their board of directors in 2017 was delighted with their cash flow and profits. However, they were concerned about future growth because of the changing diets of many consumers who had become more concerned about consuming milk-based products. The company had already added low-fat versions of the major products, but the board members were concerned that this would not be sufficient to sustain their growth and profits. Some possibilities that were suggested for consideration included (1) setting up a new company to produce non-dairy-based products such as almond milk and other alternatives to cow milk. All the new products would have a healthy "spin" such as the White Wave company; (2) market expansion of their existing product lines into Mexico and Central America; (3) expanding their current product offerings by adding ice cream, high-end cheeses made from goat and sheep milk, and high-end milk-based candy; and (4) a combination of one or more of these alternatives.

Note: Read the case study and answer the following question. The answer should be a minimum of 20 lines. No Plagiarism.

1. Evaluate all three alternatives offering pros and cons of each.

2. What would you recommend? Why?

WG is one of the world’s leading makers of mobile phones, with market share of approximately 20%.Unlike any of its major competitors, it is based in Narnia, a high-cost, developed country. Narnia has very limited natural resources, but has developed significant expertise over the decades in high-end precision engineering and efficient use of materials. WG is quoted on the Narnian stock exchange, where it is the largest company by market capitalisation. It has a wide shareholder base including most Narnian institutional investors and private individuals. Its largest three shareholders are institutions who each own around 2% of the company.WG was founded in the 1960s to make telephone equipment and in the 1990s managers made a strategic decision to focus on the then-tiny mobile phone market. This was partly attributable to the Narnian government being among the first to fully deregulate their telecoms market, which lead to lower call costs. Narnia and its neighbouring countries are also fairly rural, and its populations were enthusiastic early adopters of mobile phones. WG was given a particular boost in 1995 when the transmission standard they had pioneered was adopted as the basis for calls by the government in Narnia and many other governments around the world. Serving a rapidly growing market, WG quickly gained economies of scale that allowed cheaper production than competitors emerging later. WG then exploited these to open up export markets all over the world,enhancing their advantage further. Unlike many of its competitors, who subcontract their manufacturing to others, WG assembles most of its own handsets. Its factories are mostly in Narnia, where it benefits from the highly educated population and the presence of high-quality local suppliers to carry out increasingly high-tech manufacturing processes. Narnia has very good communication links, which helps suppliers to deliver rapidly. Technology is advancing all the time and WG regularly launches new, more sophisticated devices, most recently a suite of smartphones. However, the fastest-growing demand is for cheaper, basic models which just carry out voice calls and text messaging. This demand is driven by users in developing countries, who are concerned to keep costs down, but also want the status of using a well-known brand such as WG. WG has invested significant resources in building up a local sales presence in these markets, which allows it to spot trends and produce phones tailored to local tastes and languages. Competition in the industry is intense, and has become more so due to a recent global economic downturn. The Narnian government has also announced new anti-pollution measures that will result in large-scale manufacturers having to pay more than previously to dispose of their waste products. Shortly afterwards, WG announced that they will increase the proportion of handsets manufactured in lower-cost countries from 15% to 40% over the next three years. Component manufacturers announced plans to follow them to the new locations. This will involve cutting over 1,000 jobs in Narnia. A spokesman for the Narnian government called the decision “disappointing”. A trade union official said, “WG has increasingly been putting pressure on its suppliers to lower costs and respond more quickly to market fluctuations. This has made it unprofitable for them to operate in Narnia and lead to decisions like this”. Required:

(a) Analyse WG’s environment using two appropriate models

(b) Discuss the main stakeholders in WG and how management could try to retain their support as it seeks to reduce costs.

The function print_mean() that you wrote in the previous lesson calculates an average value and prints it on the screen. Change this function so that instead of printing the average it returns the average.In order to calculate the sum, you won't need to form a loop; call the function column_sum() instead.

# columns are [0]title [1]year [2]rating [3]length(min) [4]genre [5]budget($mil) [6]box_office_gross($mil)
oscar_data = [
["The Shape of Water", 2017, 6.914, 123, ['sci-fi', 'drama'], 19.4, 195.243464],
["Moonlight", 2016, 6.151, 110, ['drama'], 1.5, 65.046687],
["Spotlight", 2015, 7.489, 129, ['drama', 'crime', 'history'], 20.0, 88.346473],
["Birdman", 2014, 7.604, 119, ['drama', 'comedy'], 18.0, 103.215094],
["12 Years a Slave", 2013, 7.71, 133, ['drama', 'biography', 'history'], 20.0, 178.371993],
["Argo", 2012, 7.517, 120, ['thriller', 'drama', 'biography'], 44.5, 232.324128],
["The Artist", 2011, 7.942, 96, ['drama', 'melodrama', 'comedy'], 15.0, 133.432856],
["The King\'s Speech", 2010, 7.977, 118, ['drama', 'biography', 'history'], 15.0, 414.211549],
["The Hurt Locker", 2008, 7.298, 126, ['thriller', 'drama', 'war', 'history'], 15.0, 49.230772],
["Slumdog Millionaire", 2008, 7.724, 120, ['drama', 'melodrama'], 15.0, 377.910544],
["No Country for Old Men", 2007, 7.726, 122, ['thriller', 'drama', 'crime'], 25.0, 171.627166],
["The Departed", 2006, 8.456, 151, ['thriller', 'drama', 'crime'], 90.0, 289.847354],
["Crash", 2004, 7.896, 108, ['thriller', 'drama', 'crime'], 6.5, 98.410061],
["Million Dollar Baby", 2004, 8.075, 132, ['drama', 'sport'], 30.0, 216.763646],
["The Lord of the Rings: Return of the King", 2003, 8.617, 201, ['fantasy', 'drama', 'adventure'], 94.0, 1119.110941],
["Chicago", 2002, 7.669, 113, ['musical', 'comedy', 'crime'], 45.0, 306.776732],
['A Beautiful Mind', 2001, 8.557, 135, ['drama', 'biography', 'melodrama'], 58.0, 313.542341],
["Gladiator", 2000, 8.585, 155, ['action', 'drama', 'adventure'], 103.0, 457.640427],
["American Beauty", 1999, 7.965, 122, ['drama'], 15.0, 356.296601],
["Shakespeare in Love", 1998, 7.452, 123, ['drama', 'melodrama', 'comedy', 'history'], 25.0, 289.317794],
["Titanic", 1997, 8.369, 194, ['drama', 'melodrama'], 200.0, 2185.372302],
["The English Patient", 1996, 7.849, 155, ['drama', 'melodrama', 'war'], 27.0, 231.976425],
["Braveheart", 1995, 8.283, 178, ['drama', 'war', 'biography', 'history'], 72.0, 210.409945],
["Forrest Gump", 1994, 8.915, 142, ['drama', 'melodrama'], 55.0, 677.386686],
["Schindler\'s List", 1993, 8.819, 195, ['drama', 'biography', 'history'], 22.0, 321.265768],
["Unforgiven", 1992, 7.858, 131, ['drama', 'western'], 14.4, 159.157447],
["Silence of the Lambs", 1990, 8.335, 114, ['thriller', 'crime', 'mystery', 'drama', 'horror'], 19.0, 272.742922],
["Dances with Wolves", 1990, 8.112, 181, ['drama', 'adventure', 'western'], 22.0, 424.208848],
["Driving Miss Daisy", 1989, 7.645, 99, ['drama'], 7.5, 145.793296],
["Rain Man", 1988, 8.25, 133, ['drama'], 25.0, 354.825435],
]

def column_sum(data, column):
result = 0
for row in data:
result += row[column]
return result

def column_mean(data, column):
# < write code here >
  

mean_score = column_mean(oscar_data, 2)
print('Average rating: {:.2f}'.format(mean_score))

mean_length = column_mean(oscar_data, 3)
print('Average length: {:.2f} min.'.format(mean_length))

mean_budget = column_mean(oscar_data, 5)
print('Average budget: ${:.2f} mil.'.format(mean_budget))

mean_gross = column_mean(oscar_data, 6)
print('Average revenue: ${:.2f} mil.'.format(mean_gross))

# columns are [0]title [1]year [2]rating [3]length(min) [4]genre [5]budget($mil) [6]box_office_gross($mil)
oscar_data = [
["The Shape of Water", 2017, 6.914, 123, ['sci-fi', 'drama'], 19.4, 195.243464],
["Moonlight", 2016, 6.151, 110, ['drama'], 1.5, 65.046687],
["Spotlight", 2015, 7.489, 129, ['drama', 'crime', 'history'], 20.0, 88.346473],
["Birdman", 2014, 7.604, 119, ['drama', 'comedy'], 18.0, 103.215094],
["12 Years a Slave", 2013, 7.71, 133, ['drama', 'biography', 'history'], 20.0, 178.371993],
["Argo", 2012, 7.517, 120, ['thriller', 'drama', 'biography'], 44.5, 232.324128],
["The Artist", 2011, 7.942, 96, ['drama', 'melodrama', 'comedy'], 15.0, 133.432856],
["The King\'s Speech", 2010, 7.977, 118, ['drama', 'biography', 'history'], 15.0, 414.211549],
["The Hurt Locker", 2008, 7.298, 126, ['thriller', 'drama', 'war', 'history'], 15.0, 49.230772],
["Slumdog Millionaire", 2008, 7.724, 120, ['drama', 'melodrama'], 15.0, 377.910544],
["No Country for Old Men", 2007, 7.726, 122, ['thriller', 'drama', 'crime'], 25.0, 171.627166],
["The Departed", 2006, 8.456, 151, ['thriller', 'drama', 'crime'], 90.0, 289.847354],
["Crash", 2004, 7.896, 108, ['thriller', 'drama', 'crime'], 6.5, 98.410061],
["Million Dollar Baby", 2004, 8.075, 132, ['drama', 'sport'], 30.0, 216.763646],
["The Lord of the Rings: Return of the King", 2003, 8.617, 201, ['fantasy', 'drama', 'adventure'], 94.0, 1119.110941],
["Chicago", 2002, 7.669, 113, ['musical', 'comedy', 'crime'], 45.0, 306.776732],
['A Beautiful Mind', 2001, 8.557, 135, ['drama', 'biography', 'melodrama'], 58.0, 313.542341],
["Gladiator", 2000, 8.585, 155, ['action', 'drama', 'adventure'], 103.0, 457.640427],
["American Beauty", 1999, 7.965, 122, ['drama'], 15.0, 356.296601],
["Shakespeare in Love", 1998, 7.452, 123, ['drama', 'melodrama', 'comedy', 'history'], 25.0, 289.317794],
["Titanic", 1997, 8.369, 194, ['drama', 'melodrama'], 200.0, 2185.372302],
["The English Patient", 1996, 7.849, 155, ['drama', 'melodrama', 'war'], 27.0, 231.976425],
["Braveheart", 1995, 8.283, 178, ['drama', 'war', 'biography', 'history'], 72.0, 210.409945],
["Forrest Gump", 1994, 8.915, 142, ['drama', 'melodrama'], 55.0, 677.386686],
["Schindler\'s List", 1993, 8.819, 195, ['drama', 'biography', 'history'], 22.0, 321.265768],
["Unforgiven", 1992, 7.858, 131, ['drama', 'western'], 14.4, 159.157447],
["Silence of the Lambs", 1990, 8.335, 114, ['thriller', 'crime', 'mystery', 'drama', 'horror'], 19.0, 272.742922],
["Dances with Wolves", 1990, 8.112, 181, ['drama', 'adventure', 'western'], 22.0, 424.208848],
["Driving Miss Daisy", 1989, 7.645, 99, ['drama'], 7.5, 145.793296],
["Rain Man", 1988, 8.25, 133, ['drama'], 25.0, 354.825435],
]

def column_sum(data, column):
result = 0
for row in data:
result += row[column]
return result

def column_mean(data, column):
total = column_sum(oscar_data, 6)
mean = total / len(data)
return mean

# < write code here >
  

mean_score = column_mean(oscar_data, 2)
print('Average rating: {:.2f}'.format(mean_score))

mean_length = column_mean(oscar_data, 3)
print('Average length: {:.2f} min.'.format(mean_length))

mean_budget = column_mean(oscar_data, 5)
print('Average budget: ${:.2f} mil.'.format(mean_budget))

mean_gross = column_mean(oscar_data, 6)
print('Average revenue: ${:.2f} mil.'.format(mean_gross))