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excel 代写-ECON 254-001

时间：2021-04-06

University of Waterloo

Department of Economics

ECON 254-001

Assignment #2 Winter Term 2021

(Excel and MS Word/.pdf files due in LEARN drop box: April 9, 2021 by 11pm)

CHOOSE AN NBA TEAM

THEN USE THESE INSTRUCTIONS TO COMPLETE THE ASSIGNMENT

Find the row with the last two numbers of your Student ID#. Choose the NBA team in that row.

For example, when I went to UW my Student ID# ended in 66 so I would choose the New York

Knicks.

Student ID # NBA

XXXXXX01 to XXXXXX03 Atlanta Hawks

XXXXXX04 to XXXXXX06 Boston Celtics

XXXXXX07 to XXXXXX10 Brooklyn Nets

XXXXXX11 to XXXXXX13 Charlotte Hornets

XXXXXX14 to XXXXXX16 Toronto Raptors

XXXXXX17 to XXXXXX20 Cleveland Cavaliers

XXXXXX21 to XXXXXX23 Dallas Mavericks

XXXXXX24 to XXXXXX26 Denver Nuggets

XXXXXX27 to XXXXXX30 Detroit Pistons

XXXXXX31 to XXXXXX33 Golden State Warriors

XXXXXX34 to XXXXXX36 Houston Rockets

XXXXXX37 to XXXXXX40 Indiana Pacers

XXXXXX41 to XXXXXX43 LA Clippers

XXXXXX44 to XXXXXX46 LA Lakers

XXXXXX47 to XXXXXX50 Memphis Grizzlies

XXXXXX51 to XXXXXX53 Miami Heat

XXXXXX54 to XXXXXX56 Milwaukee Bucks

XXXXXX57 to XXXXXX60 Minnesota Timberwolves

XXXXXX61 to XXXXXX63 New Orleans Pelicans

XXXXXX64 to XXXXXX66 New York Knicks

XXXXXX67 to XXXXXX70 OKC Thunder

XXXXXX71 to XXXXXX73 Orlando Magic

XXXXXX74 to XXXXXX76 Philadelphia 76ers

XXXXXX77 to XXXXXX80 Phoenix Suns

XXXXXX81 to XXXXXX83 Portland Trail Blazers

XXXXXX84 to XXXXXX86 Sacramento Kings

XXXXXX87 to XXXXXX90 San Antonio Spurs

XXXXXX91 to XXXXXX93 Toronto Raptors

XXXXXX94 to XXXXXX96 Utah Jazz

XXXXXX97 to XXXXXX00 Washington Wizards

Suppose that you are a player agent for some of the members of the NBA team that you are

investigating in this assignment. Your task is to collect salary and performance data for the

players on your NBA team for the 2018/19 season and assemble it in an excel file as follows1:

Column For NBA teams:

A Player’s Name

B Player’s 2018/19 Salary

C Career Games up to the start of 2018/19

D Minutes/game in 2018/19

E Points/game in 2018/19

F Assists/game in 2018/19

G Rebounds/game in 2018/19

H Block + Steals/game in 2018/19

I Field Goal % in 2018/19

Once you have acquired and assembled your data, you will need to perform the following

regression with “Salary” as the dependent variable:

*NBA (only use players with at least 15 games played in 2018/19)2

= 0 + 1 + 2 + 3 + 4 + 5 + 6 + 7 %

ℎ,

= 2018 − 2019

= ℎ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

=

( + )

⁄ 2018 − 2019

% = 2018 − 2019

1 as before, any data that you collect will need to be documented with respect to your source(s).

2 avoid including players on 10-day contracts and other such arrangements. Also, if a player is traded mid-season

and is listed as having played more than 15 games you should include them in your regression. Imagine that they

played for your team all season to avoid additional research/headaches.

Computing Requirements

This assignment requires you to use an Excel Add-In called the Analysis ToolPak. All Computer

Labs on campus have this Add-In available. Your personal version of Excel is likely to have this

Add-In, as well. Once you have enabled the “Analysis ToolPak” then you will need to know how

to use it...a nice tutorial is available on YouTube at the link shown here:

https://www.youtube.com/watch?v=TkiB1xBnjn4

Problem #1

Present your regression results as in the example below:

“your team name” Regression Results

= 812,997.11 + 1,409.59 + 934.88 + 400,789.76 + 222,983.51 + 654,054.62 − 211,768.45 − 68,113.21%

= # ̅2 = 0. ? ? ? ? 2018 − 2019

ℎ,

= 2018 − 2019

= ℎ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

=

( + )

⁄ 2018 − 2019

% = 2018 − 2019

Problem #2

Referring to the regression results that you obtained in Problem #1 (your regression), is there a

variable that you expected to have a different impact on salaries in the NBA? Discuss why this

variable did not have the value that you expected?

(0.143) (0.000) (0.000) (0.000) (0.056) (0.000) (0.586) (0.019)

p-values

Problem #3

Referring to the regression results that you obtained in Problem #1 (your regression), what

does your ̅2 say about the quality of your regression3?

Problem #4

Suppose that the overall NBA regression results for the 2018/19 NBA season were:

Overall NBA Regression Results

= 811,998.11 + 1,379.79 + 912.48 + 389,371.45 + 242,001.58 + 202,053.54 − 179,470.49 − 64,011.95%

= 476 ̅2 = 0.9458 2018 − 2019

ℎ,

= 2018 − 2019

= ℎ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

=

( + )

⁄ 2018 − 2019

% = 2018 − 2019

Compare your team results with the “true” results in terms of coefficient magnitude, statistical

significance, and overall fit (i.e. ̅2). Your results are likely quite different. That is to be

expected.

Problem #5

What does your team’s management group seem to find more valuable in a player than the

overall results for the NBA? What does your team’s management find less valuable in a player

than the overall results for the NBA?

3 Your regression results are not likely to be very good at all. Your ̅2 will probably be low. Do not worry! The

sample size is low and the specific team salary composition will dictate how the coefficients are determined. For

example, if your team has a player that is highly paid and gets a lot of rebounds but is only an average scorer then

your rebound coefficient will be skewed upward…

(0.143) (0.000) (0.000) (0.000) (0.056) (0.000) (0.586) (0.019)

p-values

Problem #6

Using the “true regression results” given in Problem #4, calculate (in cell) each of your players’

estimated salary based on their 2018/2019 performance. Create a table containing the results

of your calculations like the example below and include it in your Word or .pdf report.

Which players on your team were overpaid? Which players on your team were underpaid? For

example, for the Utah Jazz (partial numbers from a different season)...

Name 201?/1? Salary Estimated Salary

Difference (actual -

est.) Effect

Alec Burks 2020200 2932962 -912762 underpaid

DeMarre Carroll 1705451 2574099 -868648 underpaid

Jeremy Evans 762195 -1011680 1773875 overpaid

Derrick Favors 4443360 5477743 -1034383 underpaid

Devin Harris 9319000 4966966 4352034 overpaid

Gordon Hayward 2532960 5650650 -3117690 underpaid

Josh Howard 2150000 4656432 -2506432 underpaid

Al Jefferson 14000000 11567127 2432873 overpaid

Enes Kanter 4133280 2516826 1616454 overpaid

C.J. Miles 3700000 3895287 -195287 underpaid

Problem #7

It is possible that the regression specification is missing an omitted variable. Think of a variable

that we might have left out that could help explain the variations in salary.

What variable did you think of and why might it be related to how players are paid in the NBA?

Problem #8

Collect data on your new variable and add it to your data from Problem #1. Perform a new

regression that includes all of the previous variables and your chosen variable. Report your

new regression results that include your new variable in the same format as Problem #1.

Did this “new” variable increase your ̅2 compared to your ̅2 from Problem #1? If so, why? If

not, why not?

SUMMARY OF GRADING RUBRIC:

Data Collection 2 points

Data Construction 3 points

Problem #1 – Present regression results 3 points

Problem #2 – Variable impact analysis 3 points

Problem #3 – ̅2 analysis 2 points

Problem #4 – Regression comparisons 5 points

Problem #5 – Team management analysis 2 points

Problem #6 – Over/Under table 5 points

Problem #7 – Identify a potential omitted variable 5 points

Problem #8 – Present “new” regression results 5 points

Overall quality of thought 5 points

Presentation Quality and Neatness 10 points

Total Grade 50 points

DELIVERY AND DUE DATE:

The submission of this assignment has two components. Please submit

• one (1) Excel file containing your data, as described above in the Assignment #2 drop

box on LEARN by the date stated. Please arrange your Excel file containing your data in

the following tabs (with appropriate data references):

➢ DATA

➢ Problem #1 Regression

➢ Problem #6 Over-Under

➢ Problem #8 Regression

• one (1) Word or .pdf file containing your answers to the questions as described above in

the Assignment #2 drop box on LEARN by the date stated. Begin with a Title Page with, at

minimum, the following Information:

ECON 254 Assignment #2, Your Team, First name, Last name, and your SID#.

Your submission should be double spaced with standard margins. There is not a specific page

limit for this assignment but – as ever – concise writing and analysis is appreciated by the

marker.

学霸联盟

Department of Economics

ECON 254-001

Assignment #2 Winter Term 2021

(Excel and MS Word/.pdf files due in LEARN drop box: April 9, 2021 by 11pm)

CHOOSE AN NBA TEAM

THEN USE THESE INSTRUCTIONS TO COMPLETE THE ASSIGNMENT

Find the row with the last two numbers of your Student ID#. Choose the NBA team in that row.

For example, when I went to UW my Student ID# ended in 66 so I would choose the New York

Knicks.

Student ID # NBA

XXXXXX01 to XXXXXX03 Atlanta Hawks

XXXXXX04 to XXXXXX06 Boston Celtics

XXXXXX07 to XXXXXX10 Brooklyn Nets

XXXXXX11 to XXXXXX13 Charlotte Hornets

XXXXXX14 to XXXXXX16 Toronto Raptors

XXXXXX17 to XXXXXX20 Cleveland Cavaliers

XXXXXX21 to XXXXXX23 Dallas Mavericks

XXXXXX24 to XXXXXX26 Denver Nuggets

XXXXXX27 to XXXXXX30 Detroit Pistons

XXXXXX31 to XXXXXX33 Golden State Warriors

XXXXXX34 to XXXXXX36 Houston Rockets

XXXXXX37 to XXXXXX40 Indiana Pacers

XXXXXX41 to XXXXXX43 LA Clippers

XXXXXX44 to XXXXXX46 LA Lakers

XXXXXX47 to XXXXXX50 Memphis Grizzlies

XXXXXX51 to XXXXXX53 Miami Heat

XXXXXX54 to XXXXXX56 Milwaukee Bucks

XXXXXX57 to XXXXXX60 Minnesota Timberwolves

XXXXXX61 to XXXXXX63 New Orleans Pelicans

XXXXXX64 to XXXXXX66 New York Knicks

XXXXXX67 to XXXXXX70 OKC Thunder

XXXXXX71 to XXXXXX73 Orlando Magic

XXXXXX74 to XXXXXX76 Philadelphia 76ers

XXXXXX77 to XXXXXX80 Phoenix Suns

XXXXXX81 to XXXXXX83 Portland Trail Blazers

XXXXXX84 to XXXXXX86 Sacramento Kings

XXXXXX87 to XXXXXX90 San Antonio Spurs

XXXXXX91 to XXXXXX93 Toronto Raptors

XXXXXX94 to XXXXXX96 Utah Jazz

XXXXXX97 to XXXXXX00 Washington Wizards

Suppose that you are a player agent for some of the members of the NBA team that you are

investigating in this assignment. Your task is to collect salary and performance data for the

players on your NBA team for the 2018/19 season and assemble it in an excel file as follows1:

Column For NBA teams:

A Player’s Name

B Player’s 2018/19 Salary

C Career Games up to the start of 2018/19

D Minutes/game in 2018/19

E Points/game in 2018/19

F Assists/game in 2018/19

G Rebounds/game in 2018/19

H Block + Steals/game in 2018/19

I Field Goal % in 2018/19

Once you have acquired and assembled your data, you will need to perform the following

regression with “Salary” as the dependent variable:

*NBA (only use players with at least 15 games played in 2018/19)2

= 0 + 1 + 2 + 3 + 4 + 5 + 6 + 7 %

ℎ,

= 2018 − 2019

= ℎ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

=

( + )

⁄ 2018 − 2019

% = 2018 − 2019

1 as before, any data that you collect will need to be documented with respect to your source(s).

2 avoid including players on 10-day contracts and other such arrangements. Also, if a player is traded mid-season

and is listed as having played more than 15 games you should include them in your regression. Imagine that they

played for your team all season to avoid additional research/headaches.

Computing Requirements

This assignment requires you to use an Excel Add-In called the Analysis ToolPak. All Computer

Labs on campus have this Add-In available. Your personal version of Excel is likely to have this

Add-In, as well. Once you have enabled the “Analysis ToolPak” then you will need to know how

to use it...a nice tutorial is available on YouTube at the link shown here:

https://www.youtube.com/watch?v=TkiB1xBnjn4

Problem #1

Present your regression results as in the example below:

“your team name” Regression Results

= 812,997.11 + 1,409.59 + 934.88 + 400,789.76 + 222,983.51 + 654,054.62 − 211,768.45 − 68,113.21%

= # ̅2 = 0. ? ? ? ? 2018 − 2019

ℎ,

= 2018 − 2019

= ℎ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

=

( + )

⁄ 2018 − 2019

% = 2018 − 2019

Problem #2

Referring to the regression results that you obtained in Problem #1 (your regression), is there a

variable that you expected to have a different impact on salaries in the NBA? Discuss why this

variable did not have the value that you expected?

(0.143) (0.000) (0.000) (0.000) (0.056) (0.000) (0.586) (0.019)

p-values

Problem #3

Referring to the regression results that you obtained in Problem #1 (your regression), what

does your ̅2 say about the quality of your regression3?

Problem #4

Suppose that the overall NBA regression results for the 2018/19 NBA season were:

Overall NBA Regression Results

= 811,998.11 + 1,379.79 + 912.48 + 389,371.45 + 242,001.58 + 202,053.54 − 179,470.49 − 64,011.95%

= 476 ̅2 = 0.9458 2018 − 2019

ℎ,

= 2018 − 2019

= ℎ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

= ⁄ 2018 − 2019

=

( + )

⁄ 2018 − 2019

% = 2018 − 2019

Compare your team results with the “true” results in terms of coefficient magnitude, statistical

significance, and overall fit (i.e. ̅2). Your results are likely quite different. That is to be

expected.

Problem #5

What does your team’s management group seem to find more valuable in a player than the

overall results for the NBA? What does your team’s management find less valuable in a player

than the overall results for the NBA?

3 Your regression results are not likely to be very good at all. Your ̅2 will probably be low. Do not worry! The

sample size is low and the specific team salary composition will dictate how the coefficients are determined. For

example, if your team has a player that is highly paid and gets a lot of rebounds but is only an average scorer then

your rebound coefficient will be skewed upward…

(0.143) (0.000) (0.000) (0.000) (0.056) (0.000) (0.586) (0.019)

p-values

Problem #6

Using the “true regression results” given in Problem #4, calculate (in cell) each of your players’

estimated salary based on their 2018/2019 performance. Create a table containing the results

of your calculations like the example below and include it in your Word or .pdf report.

Which players on your team were overpaid? Which players on your team were underpaid? For

example, for the Utah Jazz (partial numbers from a different season)...

Name 201?/1? Salary Estimated Salary

Difference (actual -

est.) Effect

Alec Burks 2020200 2932962 -912762 underpaid

DeMarre Carroll 1705451 2574099 -868648 underpaid

Jeremy Evans 762195 -1011680 1773875 overpaid

Derrick Favors 4443360 5477743 -1034383 underpaid

Devin Harris 9319000 4966966 4352034 overpaid

Gordon Hayward 2532960 5650650 -3117690 underpaid

Josh Howard 2150000 4656432 -2506432 underpaid

Al Jefferson 14000000 11567127 2432873 overpaid

Enes Kanter 4133280 2516826 1616454 overpaid

C.J. Miles 3700000 3895287 -195287 underpaid

Problem #7

It is possible that the regression specification is missing an omitted variable. Think of a variable

that we might have left out that could help explain the variations in salary.

What variable did you think of and why might it be related to how players are paid in the NBA?

Problem #8

Collect data on your new variable and add it to your data from Problem #1. Perform a new

regression that includes all of the previous variables and your chosen variable. Report your

new regression results that include your new variable in the same format as Problem #1.

Did this “new” variable increase your ̅2 compared to your ̅2 from Problem #1? If so, why? If

not, why not?

SUMMARY OF GRADING RUBRIC:

Data Collection 2 points

Data Construction 3 points

Problem #1 – Present regression results 3 points

Problem #2 – Variable impact analysis 3 points

Problem #3 – ̅2 analysis 2 points

Problem #4 – Regression comparisons 5 points

Problem #5 – Team management analysis 2 points

Problem #6 – Over/Under table 5 points

Problem #7 – Identify a potential omitted variable 5 points

Problem #8 – Present “new” regression results 5 points

Overall quality of thought 5 points

Presentation Quality and Neatness 10 points

Total Grade 50 points

DELIVERY AND DUE DATE:

The submission of this assignment has two components. Please submit

• one (1) Excel file containing your data, as described above in the Assignment #2 drop

box on LEARN by the date stated. Please arrange your Excel file containing your data in

the following tabs (with appropriate data references):

➢ DATA

➢ Problem #1 Regression

➢ Problem #6 Over-Under

➢ Problem #8 Regression

• one (1) Word or .pdf file containing your answers to the questions as described above in

the Assignment #2 drop box on LEARN by the date stated. Begin with a Title Page with, at

minimum, the following Information:

ECON 254 Assignment #2, Your Team, First name, Last name, and your SID#.

Your submission should be double spaced with standard margins. There is not a specific page

limit for this assignment but – as ever – concise writing and analysis is appreciated by the

marker.

学霸联盟