Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables ● margin, the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: . regress margin home if team== "Brooklyn Nets" Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1459.95122 1 80 190.65061 MS 81 206.320988 Coef. Std. Err. 8.439024 3.049595 -5.219512 2.156389 t Number of obs F(1, 80) Prob > F R-squared. Adj R-squared Root MSE P>|t| 2.77 0.007 -2.42 0.018 2.370137 -9.510864 = = = [95% Conf. Interval] The Nets average margin of victory for all games in 2014 was positive The Nets average margin of victory for all games in 2014 was negative The Nets won more games than they lost in 2014 The Nets lost more games than they won in 2014 None of the above 82 7.66 0.0070 0.0874 0.0760 13.808 14.50791 -.9281607 This output tells us the regression line equation is: margin: = -5.2 +8.4home. Assuming that the Nets played half of their games at home and half away, which of the following conclusions can we make with certainty from the regression results? (check all that apply)
Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables ● margin, the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: . regress margin home if team== "Brooklyn Nets" Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1459.95122 1 80 190.65061 MS 81 206.320988 Coef. Std. Err. 8.439024 3.049595 -5.219512 2.156389 t Number of obs F(1, 80) Prob > F R-squared. Adj R-squared Root MSE P>|t| 2.77 0.007 -2.42 0.018 2.370137 -9.510864 = = = [95% Conf. Interval] The Nets average margin of victory for all games in 2014 was positive The Nets average margin of victory for all games in 2014 was negative The Nets won more games than they lost in 2014 The Nets lost more games than they won in 2014 None of the above 82 7.66 0.0070 0.0874 0.0760 13.808 14.50791 -.9281607 This output tells us the regression line equation is: margin: = -5.2 +8.4home. Assuming that the Nets played half of their games at home and half away, which of the following conclusions can we make with certainty from the regression results? (check all that apply)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
![Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the
variables
margin, - the Net's margin of victory (number of points the Nets scored minus the number of
points their opponent scored) for game i, and
• home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in
their home arena) and equal to 0 when they are the away team (game i was played in the
opponent's arena).
I use the least-squares method to estimate the following regression model
margin = a + ßhome; + ei
Below is the Stata output corresponding to the estimated regression line:
regress margin home if team===== "Brooklyn Nets"
.
Source
Model
Residual
Total
margin
home
_cons
SS
1459.95122
15252.0488
16712
df
1459.95122
1
80 190.65061
None of the above
81 206.320988
Coef. Std. Err.
8.439024 3.049595
-5.219512 2.156389
MS
t
Number of obs
F(1, 80)
Prob > F
R-squared
O The Nets lost more games than they won in 2014
P>|t|
2.77 0.007
-2.42 0.018
Adj R-squared =
Root MSE
=
=
2.370137
-9.510864
=
The Nets average margin of victory for all games in 2014 was positive
The Nets average margin of victory for all games in 2014 was negative
The Nets won more games than they lost in 2014
[95% Conf. Interval]
82
7.66
0.0070
0.0874
0.0760
13.808
This output tells us the regression line equation is: margin = −5.2 +8.4home.
Assuming that the Nets played half of their games at home and half away, which of the following
conclusions can we make with certainty from the regression results? (check all that apply)
14.50791
-.9281607](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3f08d3cb-03f2-44ac-afec-90ed33ecc605%2Fe3fb7aab-68d2-4e5e-9afd-8cb5ba5cd0e9%2Fzivfni_processed.png&w=3840&q=75)
Transcribed Image Text:Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the
variables
margin, - the Net's margin of victory (number of points the Nets scored minus the number of
points their opponent scored) for game i, and
• home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in
their home arena) and equal to 0 when they are the away team (game i was played in the
opponent's arena).
I use the least-squares method to estimate the following regression model
margin = a + ßhome; + ei
Below is the Stata output corresponding to the estimated regression line:
regress margin home if team===== "Brooklyn Nets"
.
Source
Model
Residual
Total
margin
home
_cons
SS
1459.95122
15252.0488
16712
df
1459.95122
1
80 190.65061
None of the above
81 206.320988
Coef. Std. Err.
8.439024 3.049595
-5.219512 2.156389
MS
t
Number of obs
F(1, 80)
Prob > F
R-squared
O The Nets lost more games than they won in 2014
P>|t|
2.77 0.007
-2.42 0.018
Adj R-squared =
Root MSE
=
=
2.370137
-9.510864
=
The Nets average margin of victory for all games in 2014 was positive
The Nets average margin of victory for all games in 2014 was negative
The Nets won more games than they lost in 2014
[95% Conf. Interval]
82
7.66
0.0070
0.0874
0.0760
13.808
This output tells us the regression line equation is: margin = −5.2 +8.4home.
Assuming that the Nets played half of their games at home and half away, which of the following
conclusions can we make with certainty from the regression results? (check all that apply)
14.50791
-.9281607
Expert Solution
![](/static/compass_v2/shared-icons/check-mark.png)
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 2 steps
![Blurred answer](/static/compass_v2/solution-images/blurred-answer.jpg)
Recommended textbooks for you
![MATLAB: An Introduction with Applications](https://www.bartleby.com/isbn_cover_images/9781119256830/9781119256830_smallCoverImage.gif)
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
![Probability and Statistics for Engineering and th…](https://www.bartleby.com/isbn_cover_images/9781305251809/9781305251809_smallCoverImage.gif)
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
![Statistics for The Behavioral Sciences (MindTap C…](https://www.bartleby.com/isbn_cover_images/9781305504912/9781305504912_smallCoverImage.gif)
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
![MATLAB: An Introduction with Applications](https://www.bartleby.com/isbn_cover_images/9781119256830/9781119256830_smallCoverImage.gif)
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
![Probability and Statistics for Engineering and th…](https://www.bartleby.com/isbn_cover_images/9781305251809/9781305251809_smallCoverImage.gif)
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
![Statistics for The Behavioral Sciences (MindTap C…](https://www.bartleby.com/isbn_cover_images/9781305504912/9781305504912_smallCoverImage.gif)
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
![Elementary Statistics: Picturing the World (7th E…](https://www.bartleby.com/isbn_cover_images/9780134683416/9780134683416_smallCoverImage.gif)
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
![The Basic Practice of Statistics](https://www.bartleby.com/isbn_cover_images/9781319042578/9781319042578_smallCoverImage.gif)
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
![Introduction to the Practice of Statistics](https://www.bartleby.com/isbn_cover_images/9781319013387/9781319013387_smallCoverImage.gif)
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman