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Question 1

Our Earth Pty Ltd, an Australian owned company, are the manufacturers of a specially designed bio-degradable, disposable coffee cup made from sustainable materials.  They are currently the sole supplier to coffee shops in Australia.  During the year Our Earth Pty Ltd discovered that Coffee Bean Pty Ltd, the owner of a chain of coffee shops Australia wide, had been contracting an overseas company to manufacture a similar cup based on their design, for a cheaper price.  Coffee Bean Pty Ltd hadn’t informed its customers that it was not the original Australian made Our Earth product that they were using.  

As Our Earth Pty Ltd has a design patent over the cup they decided to take legal action against Coffee Bean Pty Ltd.  As a result of the action, the following amounts were payable by Coffee Bean Pty Ltd to Our Earth Pty Ltd in the year ended 30 June 2019:

$300,000 damages for design patent infringement$200,000 for expected lost revenue over the 12 month period that Coffee Bean Pty Ltd had been using the other product$15,000 interest received on the damages payout$40,000 reimbursement of legal fees incurred by Our Earth Pty Ltd

Required:

Advise Our Earth Pty Ltd of the taxation consequences of the above transactions .

Question 2

Sam purchased 80 acres of farmland in May 1984 for $270,000 on which he conducted a beef cattle breeding operation.  In February 1995 he purchased an additional 20 acres of adjoining farmland for $110,000 in order to expand his operation.  There was no house on the farmland so Sam lived in the nearby township. Due to ongoing drought conditions and Sam’s advancing age, he decided in 2017 to sell and retire from farming.  A local real estate agent valued the property at $440,000.  Given the time and effort Sam had put into the farm over the years he didn’t feel this was enough of a return on his investment. As the property was located close to town, the real estate suggested that Sam consider selling the land as a sub-division.  This would likely generate a higher return per acre than selling the property as farmland.

Sam re-zoned and received council approval for the sub-division in May 2017. Over the period from July 2017 to January 2018 Sam spent $450,000 on sub-division costs such as surveyor fees, electricity and water connections and main road access.  In April 2018 a local construction company agreed to buy the entire sub-division for $1,100,000.  Although the contract for sale was dated in April, settlement didn’t take place until July 2018.  Agent’s commission and legal fees payable by Sam amounted to $45,000.

Required:

Advise Sam of the taxation consequences of the above transactions .

Equipment Corporation incorporated was established on October 20, 1974. 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 Equipment Corporation for calendar year 2016. It is required to file Form 1120 and completes Form 1120-F (M-1 and M-2).

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

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

 VISUAL STUDIO (File Creation and Submissions)
 FLOWCHART
 Writing program in C++ ( cout, \n, \t, solving expressions )
 Correcting errors
Q1: Draw flow Chart of the following problems:
a) Display “Hello World” on screen.
b) Display Your Name, date of birth and mobile number on screen.
c) Compute and display the perimeter and area of a rectangle with a height of 7 inches and width
of 5 inches.
d) Compute and display the perimeter and area of a circle with a radius of 6 inches.
e) Compute and display the sum of a=1245698 and b=12345.
f) Read an employee's ID, total worked hours of a month and the amount he received per hour.
Print the employee's ID and salary of a particular month
Test Data:
Input the Employees ID: 0342
Input the working hours: 8
Salary amount/hour: 15000
Expected Output:
Employees ID = 0342
Salary = RS. 120000.00
g) Read two item’s weight and number of purchase and calculate the average value of the items
Test Data:
Weight of Item1: 15
No. of item1: 5
Weight of Item2: 25
No. of item2: 4
Expected Output:
Average Value = 19.444444
Q2: Practice the following on Visual Studio:
a) Create a blank C++ file using Microsoft Visual Studio

b) Create and save a cpp File in default directory
c) Create and save a cpp file in any location other than default location
d) Upload the test file for submission on portal.
Q3: Write C++ programs for the following problems.
a) Display Hello World message on screen.
Expected Output:
Hello World
b) Display Your Name, date of birth and mobile number on screen separated by comma as follows:
Expected Output:
Ahmad, 5-06-1995, 0321-5673451
c) Display a horizontal line of ten consecutive asterisks on screen as follows:
Expected Output:
**********
d) Display ITC LAB 1 on screen enclosed in single quotes as follows:
Expected Output:
‘ITC LAB 03’
e) Display ITC LAB 1 on screen enclosed in double quotes as follows:
Expected Output:
“ITC LAB 03”
Q4: Write the following lines of codes one by one as it is and then analyze the output:
a) cout<< ‘This is my first ITC lab ‘
b) //cout<< ‘This is my first ITC LAB’
c) cout>> “987654321”;
d) cout<<” 9876
54321”;
e) cout<<” 9876”
“54321”;
f) cout<<” 9876”;
cout<<54321;
g) cout<<”test\t\test1”;
h) cout<<” ###### \n ****** \n &&&&&&”;
i) cout<<” ###### \n &&&\t&&&”;
j) cout<<” 25+35”;
k) cout<< 100 + 200 ;
l) cout<<”40 * 3”;
cout<<”=”;
cout<<40*3;

m) cout<<10/5;
n) cout<<5/10;
o) cout<< k;

I NEED JAVASCRIPT PROGRAM

Chose an activity that you enjoy. It can be a sport that you watch or participate in, collecting, hobbies, etc. Do
not use a collection of movies since we have covered that in the assignment. Do not use the same topic as a
friend. (If two students that don’t know each other happen to select the same topic, that is fine, since they will
naturally have different property names and values.)
1. Select some aspect of the activity and create an array of objects denoting that aspect. For example, in
the case of movies, you could select movies themselves or you could select movie stars, producers,
soundtracks, etc. There are many aspects of any activity that you can use. Feel free to use the web to
research these values.
The array must contain at least 10 objects
The data must be actual data not just gibberish
Each object will have at least 4 properties with values
Each object in the array should have the same set of property names with different values
At least one string value and one number value
Other properties can be of any JavaScript value type
2. Create a function that can be used to format your object in a nice string format for output. This function
should be a pure function that takes one object as an argument and return a string. The string can be
formatted in any way that you feel is appropriate for your data. It must use one or more properties from
the object in the output string.
3. Create a new array from your array that only contains string values produced by the above function. Sort
this array alphabetically and display an appropriate heading followed by this list one string per line.
4. Prompt the user for a numeric value. Display a string (formatted using the function above) for each of the
objects that have a property (you choose) that are greater or less (up to you) the value provided by the
user. Be sure to provide a message if there are no objects that match.
5. Calculate the average value based on some property of your objects that makes sense. Output it with
descriptive text in a nicely formatted way.

Sample Output:
Movie Titles:
12 Angry Men (1957) by Sidney Lumet
Chak de! India (2007) by Shimit Amin
Hera Pheri (2000) by Priyadarshan
La Haine (1995) by Mathieu Kassovitz
Pulp Fiction (1994) by Quentin Tarantino
Schindler's List (1983) by Steven Spielberg
The Dark Knight (2008) by Christopher Nolan
The Godfather (1972) by Francis Ford Coppola
The Godfather: Part II (1974) by Francis Ford Coppola
The Good, the Bad and the Ugly (1966) by Sergio Leone
The Lord of the Rings: The Fellowship of the Ring (2001) by Peter Jackson
The Lord of the Rings: The Return of the King (2003) by Peter Jackson
The Shawshank Redemption (1994) by Frank Darabont
Movies newer than 2005:
Chak de! India (2007) by Shimit Amin
The Dark Knight (2008) by Christopher Nolan
The average age of these movies is: 31 years old.

You’re a physician in a practice group that became involved with PhyCor, Inc. Your group eliminated its entire management structure when PhyCor took over. Now, with PhyCor collapsing, you are being offered repurchase of your assets from PhyCor. What strategic planning steps do you propose to your group for going forward and selecting among options such as dissolving the group, purchasing the assets and finding another management firm, purchasing the assets and establishing in-house management, or other alternatives?

"Physician Practice Management Companies and PhyCor, Inc. Physician practice management (PPM) fi rms grew very rapidly in the late 1980s and early 1990s. PPMs promised to infuse physician practices with needed capital and provide signifi cant cost savings and increased revenues through economies of scale and improved management. They also promised to allow physicians to negotiate better contracts with the emerging HMOs and PPOs. However, by the end of the century, all of the major PPMs had gone out of business or signifi cantly downsized, with their valuations a tiny fraction of prior capitalization. Some, such as MedPartners, declared bankruptcy. Others saw their valuation plummet to almost nothing. What went wrong? This case examines the history of PPMs and the story of PhyCor, one of the prominent players.PPMs were created in response to the lack of retained earnings and marginal management that existed in many physician practices and the growth of HMOs and PPOs. As a result of increased managed care, physician organizations/medical groups experienced increased costs and lower net revenues. HMOs and PPOs also demanded large discounts from physicians. Capital was also needed to buy out senior partners, install information systems, and change their structures and governance. PPMs with signifi cant venture and Wall Street capital backing purchased prestigious medical groups, consolidated independent practices, and acquired staff clinics being divested by HMOs. Consolidation of PPMs left three large companies by the early 1990s.315Many of the physician practices signed 30- to 40-year management services contracts with the PPMs. These most often specifi ed that physicians would receive a split of revenues after payment of clinic expenses. The lower cost of capital, centralized purchasing, and greater bargaining leverage with insurer organizations were to lower costs and increase revenues.Phycor, Inc., incorporated in 1988, became by 1995 a medical network management company that managed multispecialty medical clinics and other physician organizations, provided contract management services to physician networks owned by health systems, and developed and managed independent practice associations (IPAs).1 The company also provided health care decision-support services, including demand management and disease management services, to managed care organizations, health care providers, employers, and other group associations."

This is t a relational database please write SQL queries to solve the listed questions.

The database is a variation of the “Movie Database” . There are several differences in it, so look it over carefully before writing your SQL queries

Notes:

• TheaterNum, MovieNum, and ActorNum are numeric primary key fields in their respective tables. Movie and actor names are not assumed to be unique unless specified otherwise in a question.
• In the THEATER table, Capacity is the number of seats in a theater.
• In the MOVIE table, Year is the year a movie was filmed.
• The ACTED IN table lists all of the actors who acted in a movie. The Star field can have the values Y (yes) or N (no). Assume there is only one star in each movie.

CITY

THEATER

MOVIE

SHOWINGS

DIRECTOR

PRODUCER

ACTOR

PREVIOUSJOB

ACTEDIN

NEWSPAPER

REVIEWER

REVIEW

Questions

Remember to follow all of the instructions listed on the first page.

1. Which theater(s) in Tennessee have the largest capacity?

2. How many reviews were written for each movie directed by John Carter that were filmed in the period 2014 to 2019

3. List the phone number of every theater in Tennessee. Order the results by theater number.

4. What was the total revenue generated by movies made in 2015 that were both directed by James Smith and produced by Mary Jones?

5. Assume there is only one movie titled, “The Matrix.” Who reviewed it?

6. List the cities in Tennessee that have theaters with capacities of at least 200 seats. List the cities in alphabetic order.

7. Which movies have generated more revenue than the movie directed by John Carter in 2010 that generated the most revenue of the movies he directed that year?

8. Which theaters in Tennessee, Arkansas, or Mississippi (you may use 2-letter abbreviations) showed movies whose titles began with any of the letters R, S, or T? List the theaters in numeric order.

9. Who is the oldest actor who starred in a movie made between 1995 and 2005 that was both directed by James Smith and produced by Mary Jones?

10. What was the total revenue generated by movies produced by each producer from 2010 to 2018 that starred an actor who is currently under 40 years of age? Only include producers whose movies generated more than a total of $75,000,000.

Year    Name    MinPressure_before      Gender_MF       Category        alldeaths
1950    Easy    958     1       3       2
1950    King    955     0       3       4
1952    Able    985     0       1       3
1953    Barbara 987     1       1       1
1953    Florence        985     1       1       0
1954    Carol   960     1       3       60
1954    Edna    954     1       3       20
1954    Hazel   938     1       4       20
1955    Connie  962     1       3       0
1955    Diane   987     1       1       200
1955    Ione    960     0       3       7
1956    Flossy  975     1       2       15
1958    Helene  946     1       3       1
1959    Debra   984     1       1       0
1959    Gracie  950     1       3       22
1960    Donna   930     1       4       50
1960    Ethel   981     1       1       0
1961    Carla   931     1       4       46
1963    Cindy   996     1       1       3
1964    Cleo    968     1       2       3
1964    Dora    966     1       2       5
1964    Hilda   950     1       3       37
1964    Isbell  974     1       2       3
1965    Betsy   948     1       3       75
1966    Alma    982     1       2       6
1966    Inez    983     1       1       3
1967    Beulah  950     1       3       15
1968    Gladys  977     1       2       3
1969    Camille 909     1       5       256
1970    Celia   945     1       3       22
1971    Edith   978     1       2       0
1971    Fern    979     1       1       2
1971    Ginger  995     1       1       0
1972    Agnes   980     1       1       117
1974    Carmen  952     1       3       1
1975    Eloise  955     1       3       21
1976    Belle   980     1       1       5
1977    Babe    995     1       1       0
1979    Bob     986     0       1       1
1979    David   970     0       2       15
1979    Frederic        946     0       3       5
1980    Allen   945     0       3       2
1983    Alicia  962     1       3       21
1984    Diana   949     1       2       3
1985    Bob     1002    0       1       0
1985    Danny   987     0       1       1
1985    Elena   959     1       3       4
1985    Gloria  942     1       3       8
1985    Juan    971     0       1       12
1985    Kate    967     1       2       5
1986    Bonnie  990     1       1       3
1986    Charley 990     0       1       5
1987    Floyd   993     0       1       0
1988    Florence        984     1       1       1
1989    Chantal 986     1       1       13
1989    Hugo    934     0       4       21
1989    Jerry   983     0       1       3
1991    Bob     962     0       2       15
1992    Andrew  922     0       5       62
1993    Emily   960     1       3       3
1995    Erin    973     1       2       6
1995    Opal    942     1       3       9
1996    Bertha  974     1       2       8
1996    Fran    954     1       3       26
1997    Danny   984     0       1       10
1998    Bonnie  964     1       2       3
1998    Earl    987     0       1       3
1998    Georges 964     0       2       1
1999    Bret    951     0       3       0
1999    Floyd   956     0       2       56
1999    Irene   987     1       1       8
2002    Lili    963     1       1       2
2003    Claudette       979     1       1       3
2003    Isabel  957     1       2       51
2004    Alex    972     0       1       1
2004    Charley 941     0       4       10
2004    Frances 960     1       2       7
2004    Gaston  985     0       1       8
2004    Ivan    946     0       3       25
2004    Jeanne  950     1       3       5
2005    Cindy   991     1       1       1
2005    Dennis  946     0       3       15
2005    Ophelia 982     1       1       1
2005    Rita    937     1       3       62
2005    Wilma   950     1       3       5
2005    Katrina 902     1       3       1833
2007    Humberto        985     0       1       1
2008    Dolly   963     1       1       1
2008    Gustav  951     0       2       52
2008    Ike     935     0       2       84
2011    Irene   952     1       1       41
2012    Isaac   965     0       1       5
2012    Sandy   945     1       2       159
                                        

Open Hurricane data.

SETUP: Is it reasonable to assume that average hurricane pressure for category 4 is different from that of category 1? Given the data, your job is to check if this assertion is indeed reasonable or not. HINT: Read Lecture 24.

19. What would be the correct Null-Hypothesis?

• a. Data related to two different categories should not be related.
• b. The population averages are equal.
• c. The slope of the regression line is equal to zero.
• d. None of these.

20. The P-value is 3.33E-09. What can be statistically concluded?

• a. We reject the Null Hypothesis.
• b. We accept the Null Hypothesis.
• c. We cannot reject the Null Hypothesis.
• d. None of these.

21. Write a one-line additional comment.

• a. We cannot conclude that data related to two different hurricane categories are related.
• b. We are confident that hurricanes with category 4 has different pressure than those of category 1.
• c. We cannot conclude that hurricanes with category 4 has lower pressure than those of category 1.
• d. None of these.

Year    Name    MinPressure_before      Gender_MF       Category        alldeaths
1950    Easy    958     1       3       2
1950    King    955     0       3       4
1952    Able    985     0       1       3
1953    Barbara 987     1       1       1
1953    Florence        985     1       1       0
1954    Carol   960     1       3       60
1954    Edna    954     1       3       20
1954    Hazel   938     1       4       20
1955    Connie  962     1       3       0
1955    Diane   987     1       1       200
1955    Ione    960     0       3       7
1956    Flossy  975     1       2       15
1958    Helene  946     1       3       1
1959    Debra   984     1       1       0
1959    Gracie  950     1       3       22
1960    Donna   930     1       4       50
1960    Ethel   981     1       1       0
1961    Carla   931     1       4       46
1963    Cindy   996     1       1       3
1964    Cleo    968     1       2       3
1964    Dora    966     1       2       5
1964    Hilda   950     1       3       37
1964    Isbell  974     1       2       3
1965    Betsy   948     1       3       75
1966    Alma    982     1       2       6
1966    Inez    983     1       1       3
1967    Beulah  950     1       3       15
1968    Gladys  977     1       2       3
1969    Camille 909     1       5       256
1970    Celia   945     1       3       22
1971    Edith   978     1       2       0
1971    Fern    979     1       1       2
1971    Ginger  995     1       1       0
1972    Agnes   980     1       1       117
1974    Carmen  952     1       3       1
1975    Eloise  955     1       3       21
1976    Belle   980     1       1       5
1977    Babe    995     1       1       0
1979    Bob     986     0       1       1
1979    David   970     0       2       15
1979    Frederic        946     0       3       5
1980    Allen   945     0       3       2
1983    Alicia  962     1       3       21
1984    Diana   949     1       2       3
1985    Bob     1002    0       1       0
1985    Danny   987     0       1       1
1985    Elena   959     1       3       4
1985    Gloria  942     1       3       8
1985    Juan    971     0       1       12
1985    Kate    967     1       2       5
1986    Bonnie  990     1       1       3
1986    Charley 990     0       1       5
1987    Floyd   993     0       1       0
1988    Florence        984     1       1       1
1989    Chantal 986     1       1       13
1989    Hugo    934     0       4       21
1989    Jerry   983     0       1       3
1991    Bob     962     0       2       15
1992    Andrew  922     0       5       62
1993    Emily   960     1       3       3
1995    Erin    973     1       2       6
1995    Opal    942     1       3       9
1996    Bertha  974     1       2       8
1996    Fran    954     1       3       26
1997    Danny   984     0       1       10
1998    Bonnie  964     1       2       3
1998    Earl    987     0       1       3
1998    Georges 964     0       2       1
1999    Bret    951     0       3       0
1999    Floyd   956     0       2       56
1999    Irene   987     1       1       8
2002    Lili    963     1       1       2
2003    Claudette       979     1       1       3
2003    Isabel  957     1       2       51
2004    Alex    972     0       1       1
2004    Charley 941     0       4       10
2004    Frances 960     1       2       7
2004    Gaston  985     0       1       8
2004    Ivan    946     0       3       25
2004    Jeanne  950     1       3       5
2005    Cindy   991     1       1       1
2005    Dennis  946     0       3       15
2005    Ophelia 982     1       1       1
2005    Rita    937     1       3       62
2005    Wilma   950     1       3       5
2005    Katrina 902     1       3       1833
2007    Humberto        985     0       1       1
2008    Dolly   963     1       1       1
2008    Gustav  951     0       2       52
2008    Ike     935     0       2       84
2011    Irene   952     1       1       41
2012    Isaac   965     0       1       5
2012    Sandy   945     1       2       159
Test if there is a significant difference in the death by Hurricanes and Min Pressure measured. Answer the questions for Assessment. (Pick the closest answer)

7. What is the P-value?

• a. #DIV/0!
• b. 0.384808843
• c. 0.634755682
• d. None of these

8. What is the Statistical interpretation?

• a. The P-value is too large to have a conclusive answer.
• b. The P-value is too small to have a conclusive answer.
• c. ​​The P-value is much smaller than 5% thus we are certain that the average of hurricane deaths is significantly different from average min pressure.
• d. None of the above.

9. What is the conclusion?

• a. The statistics does not agree with the intuition since one would expect that stronger hurricanes to be deadlier.
• b. ​​Statistical interpretation agrees with the intuition, the lower the pressure the stronger the hurricanes.
• c. Statistics confirms that hurricanes’ pressure does relate to the death count.
• d. The test does not make statistical sense, it compares “apples and oranges”.

Open Hurricanes data.

Test if there is a significant difference in the death by Hurricanes and Min Pressure measured. Answer the questions for Assessment. (Pick the closest answer)

7. What is the P-value?

• a. #DIV/0!
• b. 0.384808843
• c. 0.634755682
• d. None of these

8. What is the Statistical interpretation?

• a. The P-value is too large to have a conclusive answer.
• b. The P-value is too small to have a conclusive answer.
• c. ​​The P-value is much smaller than 5% thus we are certain that the average of hurricane deaths is significantly different from average min pressure.
• d. None of the above.

9. What is the conclusion?

• a. The statistics does not agree with the intuition since one would expect that stronger hurricanes to be deadlier.
• b. ​​Statistical interpretation agrees with the intuition, the lower the pressure the stronger the hurricanes.
• c. Statistics confirms that hurricanes’ pressure does relate to the death count.
• d. The test does not make statistical sense, it compares “apples and oranges”.

Year   Name   MinPressure_before   Gender_MF   Category   alldeaths
1950   Easy   958   1   3   2
1950   King   955   0   3   4
1952   Able   985   0   1   3
1953   Barbara   987   1   1   1
1953   Florence   985   1   1   0
1954   Carol   960   1   3   60
1954   Edna   954   1   3   20
1954   Hazel   938   1   4   20
1955   Connie   962   1   3   0
1955   Diane   987   1   1   200
1955   Ione   960   0   3   7
1956   Flossy   975   1   2   15
1958   Helene   946   1   3   1
1959   Debra   984   1   1   0
1959   Gracie   950   1   3   22
1960   Donna   930   1   4   50
1960   Ethel   981   1   1   0
1961   Carla   931   1   4   46
1963   Cindy   996   1   1   3
1964   Cleo   968   1   2   3
1964   Dora   966   1   2   5
1964   Hilda   950   1   3   37
1964   Isbell   974   1   2   3
1965   Betsy   948   1   3   75
1966   Alma   982   1   2   6
1966   Inez   983   1   1   3
1967   Beulah   950   1   3   15
1968   Gladys   977   1   2   3
1969   Camille   909   1   5   256
1970   Celia   945   1   3   22
1971   Edith   978   1   2   0
1971   Fern   979   1   1   2
1971   Ginger   995   1   1   0
1972   Agnes   980   1   1   117
1974   Carmen   952   1   3   1
1975   Eloise   955   1   3   21
1976   Belle   980   1   1   5
1977   Babe   995   1   1   0
1979   Bob   986   0   1   1
1979   David   970   0   2   15
1979   Frederic   946   0   3   5
1980   Allen   945   0   3   2
1983   Alicia   962   1   3   21
1984   Diana   949   1   2   3
1985   Bob   1002   0   1   0
1985   Danny   987   0   1   1
1985   Elena   959   1   3   4
1985   Gloria   942   1   3   8
1985   Juan   971   0   1   12
1985   Kate   967   1   2   5
1986   Bonnie   990   1   1   3
1986   Charley   990   0   1   5
1987   Floyd   993   0   1   0
1988   Florence   984   1   1   1
1989   Chantal   986   1   1   13
1989   Hugo   934   0   4   21
1989   Jerry   983   0   1   3
1991   Bob   962   0   2   15
1992   Andrew   922   0   5   62
1993   Emily   960   1   3   3
1995   Erin   973   1   2   6
1995   Opal   942   1   3   9
1996   Bertha   974   1   2   8
1996   Fran   954   1   3   26
1997   Danny   984   0   1   10
1998   Bonnie   964   1   2   3
1998   Earl   987   0   1   3
1998   Georges   964   0   2   1
1999   Bret   951   0   3   0
1999   Floyd   956   0   2   56
1999   Irene   987   1   1   8
2002   Lili   963   1   1   2
2003   Claudette   979   1   1   3
2003   Isabel   957   1   2   51
2004   Alex   972   0   1   1
2004   Charley   941   0   4   10
2004   Frances   960   1   2   7
2004   Gaston   985   0   1   8
2004   Ivan   946   0   3   25
2004   Jeanne   950   1   3   5
2005   Cindy   991   1   1   1
2005   Dennis   946   0   3   15
2005   Ophelia   982   1   1   1
2005   Rita   937   1   3   62
2005   Wilma   950   1   3   5
2005   Katrina   902   1   3   1833
2007   Humberto   985   0   1   1
2008   Dolly   963   1   1   1
2008   Gustav   951   0   2   52
2008   Ike   935   0   2   84
2011   Irene   952   1   1   41
2012   Isaac   965   0   1   5
2012   Sandy   945   1   2   159
                  

Create a class called MovieReducerExtremes that implements MediaReducer. Implement a reducer that takes a movie list and an option ("newest" or "oldest"), then return the newest or oldest movie as appropriate.Submit both the MovieReducerExtremes and the Movie class from the first question.

/////Required Output:///////

Newest\n
 2014 AKA Jessica Jones                                       Action         \n
Oldest\n
 1936 Cabaret                                                 Music          \n

Given Files:

Movie.java

public class Movie extends Media {
    public Movie(String name, int year, String genre) {
        super(name, year, genre);
    }

    public String getEra() {
        if (getYear() >= 2000) {
            return "New Millennium Era";
        } else if (getYear() >= 1977) {
            return "Modern Era";
        } else if (getYear() >= 1955) {
            return "Change Era";
        } else if (getYear() >= 1941) {
            return "Golden Era";
        }

        return "Pre-Golden Era";
    }

    public boolean wasReleasedAfter(Media other) {
        return getYear() > other.getYear();
    }

    public boolean wasReleasedBeforeOrInSameYear(Media other) {
        return getYear() <= other.getYear();
    }
}

Demo3.java

import java.io.FileNotFoundException;
import java.util.ArrayList;

public class Demo3 
{

    public static void main(String[] args) throws FileNotFoundException {

        ArrayList movies = MovieLoader.loadAllMovies();

        MediaReducer op = new MovieReducerExtremes();

        System.out.println("Newest");
        System.out.println(op.reduce(movies, "Newest"));
        System.out.println("Oldest");
        System.out.println(op.reduce(movies, "Oldest"));
    }
}

Media.java
public abstract class Media {
    private String name;
    private int year;
    private String genre;

    public Media(String n, int y, String g) {
        name = n;
        year = y;
        genre = g;
    }

    public String getName() {
        return name;
    }

    public int getYear() {
        return year;
    }

    public String getGenre() {
        return genre;
    }

    public String toString() {
        return String.format("%5d %-55s %-15s", year, name, genre);
    }

    //if the media was released on or after the year 2000, return New Millennium Era
    //if the media was released on or after the year 1977, return Modern Era
    //if the media was released on or after the year 1955, return Change Era
    //if the media was released on or after the year 1941, return Golden Era
    //in any other situation, return Pre-Golden Era
    public abstract String getEra();

    //return true if this media has a greater release year than the other's
    public abstract boolean wasReleasedAfter(Media other);

    //return true if this media was a lesser or equal release yearn than the other's
    public abstract boolean wasReleasedBeforeOrInSameYear(Media other);
}

MovieLoader.java

import java.io.File;
import java.io.FileNotFoundException;
import java.util.ArrayList;
import java.util.Scanner;

public class MovieLoader {
    public static ArrayList loadAllMovies() throws FileNotFoundException {
        File f = new File("movie_list.txt");
        Scanner inputFile = new Scanner(f);
        ArrayList result = new ArrayList<>();
        while (inputFile.hasNextLine()) {
            String name = inputFile.nextLine();
            int year = inputFile.nextInt();
            //skip new line
            inputFile.nextLine();
            String genre = inputFile.nextLine();
            Media m = new Movie(name, year, genre);
            result.add(m);
        }
        return result;
    }
}

MediaReducer

import java.util.ArrayList;

public interface MediaReducer {
    public String reduce(ArrayList list, String key);
}

A couple from the movie_list.txt

!Next?
1994
Documentary
#1 Single
2006
Reality-TV
#ByMySide
2012
Drama
#Follow
2011
Mystery
#nitTWITS
2011
Comedy
$#*! My Dad Says
2010
Comedy
$1,000,000 Chance of a Lifetime
1986
Game-Show
$100 Makeover
2010
Reality-TV
$100 Taxi Ride
2001
Documentary
$100,000 Name That Tune
1984
Game-Show
$100,000 Name That Tune
1984
Music
$2 Bill
2002
Documentary
$2 Bill
2002
Music
$2 Bill
2002
Music
$2 Bill
2002
Music
$2 Bill
2002
Music
$25 Million Dollar Hoax
2004
Reality-TV
$40 a Day
2002
Documentary
$5 Cover
2009
Drama
$5 Cover: Seattle
2009
Drama
$50,000 Letterbox
1980
Game-Show
$9.99
2003
Adventure
$weepstake$
1979
Drama
' Horse Trials '
2011
Sport
'80s Videos: A to Z
2009
Music
'Allo 'Allo!
1982
Comedy
'Allo 'Allo!
1982
War
'Conversations with My Wife'
2010
Comedy
'Da Kink in My Hair
2007
Comedy
'Da Kink in My Hair
2007
Drama
'More strasti'
2000
Romance
'Ons Sterrenkookboek'
2007
Documentary
'Orrible
2001
Comedy
'Orrible
2001
Crime
'Orrible
2001
Drama
'S ann an Ile
2009
Documentary
'Sang linggo nAPO sila
1995
Game-Show
'Sang linggo nAPO sila
1995
Musical
'T Wilhelmina
1975
Comedy
'Til Death Do Us Part
2006
Crime
'Til Death Do Us Part
2006
Drama
'Til Death Do Us Part
2006
Fantasy
'Til Death Do Us Part
2006
Romance
'Til Death Do Us Part
2006
Thriller
'Til Death
2006
Comedy
'Untold
2004
Documentary
'Wag kukurap
2004
Horror
'Way Out
1961
Drama
'Way Out
1961
Horror
'Way Out
1961
Sci-Fi
'n Shrink
2009
Comedy
't Is maar TV
1999
Comedy
't Is maar TV
1999
Game-Show
't Is maar een spel
2002
Comedy
't Is maar een spel
2002
Game-Show
't Schaep Met De 5 Pooten
1969
Comedy
't Schaep Met De 5 Pooten
2006
Comedy
't Schaep Met De 5 Pooten
2006
Drama
't Zal je gebeuren...
1998
Drama
't Zonnetje in huis
1993
Comedy
(S)truth
1999
Drama
+ Clair
2001
Documentary
+ Emprendedores mi+d
2010
Documentary
+ Investigadores
2008
Documentary
+ de cin�ma
2001
Documentary
+ de cin�ma
2001
News
... ins Gr�ne! Das Stadt-Land-Lust-Magazin
2010
Documentary
... und basta!
2006
Comedy
... und basta!
2006
Music
... und die Tuba bl�st der Huber
1981
Comedy