Is there a relationship between total team salary and the performance of football teams? For a recent season, a linear model predicting Wins (out of 16 regular season games) from the total team Salary (SM) for 32 teams in a football league is Wins -6.353 + 0.105 Salary. Complete parts a through h below. a) What is the explanatory variable? The explanatory variable is because b) What is the response variable? The response variable is because c) What does the slope mean in this context? In this league, team are, on average, about V higher for every (Type an integer or a decimal. Do not round.)

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Is there a relationship between total team salary and the performance of football teams? For a recent season, a linear model predicting Wins
(out of 16 regular season games) from the total team Salary (SM) for 32 teams in a football league is Wins = -6.353 +0.105 Salary. Complete
parts a through h below.
a) What is the explanatory variable?
The explanatory variable is
because
b) What is the response variable?
The response variable is
because
c) What does the slope mean in this context?
in this league, team
(Type an integer or a decimal. Do not round.)
are, on average, about
higher for every
d) What does the y-intercept mean in this context? Is it meaningful?
V is
This
v meaningful because it
The y-intercept is the average
of a team in this league whose
is
(Type an integer or a decimal. Do not round.)
e) If one team spends $10 million more than another on salary, how many more games on average would the first team be predicted to win?
O game(s)
(Type an integer or a decimal. Do not round.)
1) One team spent $140 million on salaries and won 13 games. Did they do better or worse than predicted?
predicted.
This team would be predicted to win game(s), so they did
(Type an integer or a decimal. Do not round.)
9) What was this team's residual?
Dgame(s)
(Type an integer or a decimal. Do not round.)
h) The residual standard deviation is 3.08 games. What does that tell you about the likely practical use of this model for predicting wins?
(Assume games cannot be tied.)
useful. The residual standard deviation says that one cannot predict better than about games. For a prediction of 8
teams are likely to fall in that interval.
This model
games won, that is to games. In a 16-game season,
(Round to the nearest whole number as needed. Use ascending order.)
Transcribed Image Text:Is there a relationship between total team salary and the performance of football teams? For a recent season, a linear model predicting Wins (out of 16 regular season games) from the total team Salary (SM) for 32 teams in a football league is Wins = -6.353 +0.105 Salary. Complete parts a through h below. a) What is the explanatory variable? The explanatory variable is because b) What is the response variable? The response variable is because c) What does the slope mean in this context? in this league, team (Type an integer or a decimal. Do not round.) are, on average, about higher for every d) What does the y-intercept mean in this context? Is it meaningful? V is This v meaningful because it The y-intercept is the average of a team in this league whose is (Type an integer or a decimal. Do not round.) e) If one team spends $10 million more than another on salary, how many more games on average would the first team be predicted to win? O game(s) (Type an integer or a decimal. Do not round.) 1) One team spent $140 million on salaries and won 13 games. Did they do better or worse than predicted? predicted. This team would be predicted to win game(s), so they did (Type an integer or a decimal. Do not round.) 9) What was this team's residual? Dgame(s) (Type an integer or a decimal. Do not round.) h) The residual standard deviation is 3.08 games. What does that tell you about the likely practical use of this model for predicting wins? (Assume games cannot be tied.) useful. The residual standard deviation says that one cannot predict better than about games. For a prediction of 8 teams are likely to fall in that interval. This model games won, that is to games. In a 16-game season, (Round to the nearest whole number as needed. Use ascending order.)
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