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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
