TABLE 15-9 Many Factors Determine the Attendance at Major LeagueThe Coefficient of Multiple Determination ( R 2 J

The Answer of TABLE 15-9 Many Factors Determine the Attendance at Major LeagueThe Coefficient of Multiple Determination ( R 2 J

TABLE 15-9 Many Factors Determine the Attendance at Major LeagueThe Coefficient of Multiple Determination ( R 2 J

The Answer of TABLE 15-9 Many Factors Determine the Attendance at Major LeagueThe Coefficient of Multiple Determination ( R 2 J

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TABLE 15-9
Many Factors Determine the Attendance at Major League

question 28

Multiple Choice

TABLE 15-9
Many factors determine the attendance at Major League Baseball games. These factors can include when the game is played, the weather, the opponent, whether or not the team is having a good season, and whether or not a marketing promotion is held. Data from 80 games of the Kansas City Royals for the following variables are collected.
ATTENDANCE = Paid attendance for the game
TEMP = High temperature for the day
WIN% = Team's winning percentage at the time of the game
OPWIN% = Opponent team's winning percentage at the time of the game WEEKEND - 1 if game played on Friday, Saturday or Sunday; 0 otherwise
PROMOTION - 1 = if a promotion was held; 0 = if no promotion was held
The regression results using attendance as the dependent variable and the remaining five variables as the independent variables are presented below.

$\begin{array}{l}\text { Regression Statistics }\\\begin{array} { l r } \hline \text { Multiple R } & 0.5487 \\\text { R Square } & 0.3011 \\\text { Adjusted R Square } & 0.2538 \\\text { Standard Error } & 6442.4456 \\\text { Observations } & 80 \\\hline\end{array}\end{array}$

$\begin{array}{l}\text { ANOVA }\\\begin{array} { l c c c c c } \hline & \mathrm { df } & \text { SS } & \text { MS } & \text { F } & \text { Significance } \mathrm { F } \\\hline \text { Regression } & 5 & 1322911703.0671 & 264582340.6134 & 6.3747 & 0.0001 \\\text { Residual } & 74 & 3071377751.1204 & 41505104.7449 & & \\\text { Total } & 79 & 4394289454.1875 & & & \\\hline\end{array}\end{array}$

$\begin{array}{lrrrr}\hline&\text{Coefficients}&\text{ Standard Error}&\text{ t Stat}&\text{p-value}\\\hline\text{Intercept}&-3862.4808&6180.9452&-0.6249&0.5340\\\text { Temp } & 51.7031 & 62.9439 & 0.8214 & 0.4140 \\\text { Win\% } & 21.1085 & 16.2338 & 1.3003 & 0.1975 \\\text { OpWin\% } & 11.3453 & 6.4617 & 1.7558 & 0.0833 \\\text { Weekend } & 367.5377 & 2786.2639 & 0.1319 & 0.8954 \\\text { Promotion } & 6927.8820 & 2784.3442 & 2.4882 & 0.0151 \\\hline\end{array}$

$TABLE 15-9 Many factors determine the attendance at Major League Baseball games. These factors can include when the game is played, the weather, the opponent, whether or not the team is having a good season, and whether or not a marketing promotion is held. Data from 80 games of the Kansas City Royals for the following variables are collected. ATTENDANCE = Paid attendance for the game TEMP = High temperature for the day WIN% = Team's winning percentage at the time of the game OPWIN% = Opponent team's winning percentage at the time of the game WEEKEND - 1 if game played on Friday, Saturday or Sunday; 0 otherwise PROMOTION - 1 = if a promotion was held; 0 = if no promotion was held The regression results using attendance as the dependent variable and the remaining five variables as the independent variables are presented below. \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l r } \hline \text { Multiple R } & 0.5487 \\ \text { R Square } & 0.3011 \\ \text { Adjusted R Square } & 0.2538 \\ \text { Standard Error } & 6442.4456 \\ \text { Observations } & 80 \\ \hline \end{array} \end{array} \begin{array}{l} \text { ANOVA }\\ \begin{array} { l c c c c c } \hline & \mathrm { df } & \text { SS } & \text { MS } & \text { F } & \text { Significance } \mathrm { F } \\ \hline \text { Regression } & 5 & 1322911703.0671 & 264582340.6134 & 6.3747 & 0.0001 \\ \text { Residual } & 74 & 3071377751.1204 & 41505104.7449 & & \\ \text { Total } & 79 & 4394289454.1875 & & & \\ \hline \end{array} \end{array} \begin{array}{lrrrr} \hline&\text{Coefficients}&\text{ Standard Error}&\text{ t Stat}&\text{p-value}\\ \hline\text{Intercept}&-3862.4808&6180.9452&-0.6249&0.5340\\ \text { Temp } & 51.7031 & 62.9439 & 0.8214 & 0.4140 \\ \text { Win\% } & 21.1085 & 16.2338 & 1.3003 & 0.1975 \\ \text { OpWin\% } & 11.3453 & 6.4617 & 1.7558 & 0.0833 \\ \text { Weekend } & 367.5377 & 2786.2639 & 0.1319 & 0.8954 \\ \text { Promotion } & 6927.8820 & 2784.3442 & 2.4882 & 0.0151 \\ \hline \end{array} The coefficient of multiple determination ( R 2 j) of each of the 5 predictors with all the other remaining predictors are, respectively, 0.2675, 0.3101, 0.1038, 0.7325, and 0.7308. -Referring to Table 15-9, which of the following assumptions is most likely violated based on the residual plot for WIN%? A) normality of errors B) linearity C) equal variance D) none of the above$

$TABLE 15-9 Many factors determine the attendance at Major League Baseball games. These factors can include when the game is played, the weather, the opponent, whether or not the team is having a good season, and whether or not a marketing promotion is held. Data from 80 games of the Kansas City Royals for the following variables are collected. ATTENDANCE = Paid attendance for the game TEMP = High temperature for the day WIN% = Team's winning percentage at the time of the game OPWIN% = Opponent team's winning percentage at the time of the game WEEKEND - 1 if game played on Friday, Saturday or Sunday; 0 otherwise PROMOTION - 1 = if a promotion was held; 0 = if no promotion was held The regression results using attendance as the dependent variable and the remaining five variables as the independent variables are presented below. \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l r } \hline \text { Multiple R } & 0.5487 \\ \text { R Square } & 0.3011 \\ \text { Adjusted R Square } & 0.2538 \\ \text { Standard Error } & 6442.4456 \\ \text { Observations } & 80 \\ \hline \end{array} \end{array} \begin{array}{l} \text { ANOVA }\\ \begin{array} { l c c c c c } \hline & \mathrm { df } & \text { SS } & \text { MS } & \text { F } & \text { Significance } \mathrm { F } \\ \hline \text { Regression } & 5 & 1322911703.0671 & 264582340.6134 & 6.3747 & 0.0001 \\ \text { Residual } & 74 & 3071377751.1204 & 41505104.7449 & & \\ \text { Total } & 79 & 4394289454.1875 & & & \\ \hline \end{array} \end{array} \begin{array}{lrrrr} \hline&\text{Coefficients}&\text{ Standard Error}&\text{ t Stat}&\text{p-value}\\ \hline\text{Intercept}&-3862.4808&6180.9452&-0.6249&0.5340\\ \text { Temp } & 51.7031 & 62.9439 & 0.8214 & 0.4140 \\ \text { Win\% } & 21.1085 & 16.2338 & 1.3003 & 0.1975 \\ \text { OpWin\% } & 11.3453 & 6.4617 & 1.7558 & 0.0833 \\ \text { Weekend } & 367.5377 & 2786.2639 & 0.1319 & 0.8954 \\ \text { Promotion } & 6927.8820 & 2784.3442 & 2.4882 & 0.0151 \\ \hline \end{array} The coefficient of multiple determination ( R 2 j) of each of the 5 predictors with all the other remaining predictors are, respectively, 0.2675, 0.3101, 0.1038, 0.7325, and 0.7308. -Referring to Table 15-9, which of the following assumptions is most likely violated based on the residual plot for WIN%? A) normality of errors B) linearity C) equal variance D) none of the above$ $TABLE 15-9 Many factors determine the attendance at Major League Baseball games. These factors can include when the game is played, the weather, the opponent, whether or not the team is having a good season, and whether or not a marketing promotion is held. Data from 80 games of the Kansas City Royals for the following variables are collected. ATTENDANCE = Paid attendance for the game TEMP = High temperature for the day WIN% = Team's winning percentage at the time of the game OPWIN% = Opponent team's winning percentage at the time of the game WEEKEND - 1 if game played on Friday, Saturday or Sunday; 0 otherwise PROMOTION - 1 = if a promotion was held; 0 = if no promotion was held The regression results using attendance as the dependent variable and the remaining five variables as the independent variables are presented below. \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l r } \hline \text { Multiple R } & 0.5487 \\ \text { R Square } & 0.3011 \\ \text { Adjusted R Square } & 0.2538 \\ \text { Standard Error } & 6442.4456 \\ \text { Observations } & 80 \\ \hline \end{array} \end{array} \begin{array}{l} \text { ANOVA }\\ \begin{array} { l c c c c c } \hline & \mathrm { df } & \text { SS } & \text { MS } & \text { F } & \text { Significance } \mathrm { F } \\ \hline \text { Regression } & 5 & 1322911703.0671 & 264582340.6134 & 6.3747 & 0.0001 \\ \text { Residual } & 74 & 3071377751.1204 & 41505104.7449 & & \\ \text { Total } & 79 & 4394289454.1875 & & & \\ \hline \end{array} \end{array} \begin{array}{lrrrr} \hline&\text{Coefficients}&\text{ Standard Error}&\text{ t Stat}&\text{p-value}\\ \hline\text{Intercept}&-3862.4808&6180.9452&-0.6249&0.5340\\ \text { Temp } & 51.7031 & 62.9439 & 0.8214 & 0.4140 \\ \text { Win\% } & 21.1085 & 16.2338 & 1.3003 & 0.1975 \\ \text { OpWin\% } & 11.3453 & 6.4617 & 1.7558 & 0.0833 \\ \text { Weekend } & 367.5377 & 2786.2639 & 0.1319 & 0.8954 \\ \text { Promotion } & 6927.8820 & 2784.3442 & 2.4882 & 0.0151 \\ \hline \end{array} The coefficient of multiple determination ( R 2 j) of each of the 5 predictors with all the other remaining predictors are, respectively, 0.2675, 0.3101, 0.1038, 0.7325, and 0.7308. -Referring to Table 15-9, which of the following assumptions is most likely violated based on the residual plot for WIN%? A) normality of errors B) linearity C) equal variance D) none of the above$

$TABLE 15-9 Many factors determine the attendance at Major League Baseball games. These factors can include when the game is played, the weather, the opponent, whether or not the team is having a good season, and whether or not a marketing promotion is held. Data from 80 games of the Kansas City Royals for the following variables are collected. ATTENDANCE = Paid attendance for the game TEMP = High temperature for the day WIN% = Team's winning percentage at the time of the game OPWIN% = Opponent team's winning percentage at the time of the game WEEKEND - 1 if game played on Friday, Saturday or Sunday; 0 otherwise PROMOTION - 1 = if a promotion was held; 0 = if no promotion was held The regression results using attendance as the dependent variable and the remaining five variables as the independent variables are presented below. \begin{array}{l} \text { Regression Statistics }\\ \begin{array} { l r } \hline \text { Multiple R } & 0.5487 \\ \text { R Square } & 0.3011 \\ \text { Adjusted R Square } & 0.2538 \\ \text { Standard Error } & 6442.4456 \\ \text { Observations } & 80 \\ \hline \end{array} \end{array} \begin{array}{l} \text { ANOVA }\\ \begin{array} { l c c c c c } \hline & \mathrm { df } & \text { SS } & \text { MS } & \text { F } & \text { Significance } \mathrm { F } \\ \hline \text { Regression } & 5 & 1322911703.0671 & 264582340.6134 & 6.3747 & 0.0001 \\ \text { Residual } & 74 & 3071377751.1204 & 41505104.7449 & & \\ \text { Total } & 79 & 4394289454.1875 & & & \\ \hline \end{array} \end{array} \begin{array}{lrrrr} \hline&\text{Coefficients}&\text{ Standard Error}&\text{ t Stat}&\text{p-value}\\ \hline\text{Intercept}&-3862.4808&6180.9452&-0.6249&0.5340\\ \text { Temp } & 51.7031 & 62.9439 & 0.8214 & 0.4140 \\ \text { Win\% } & 21.1085 & 16.2338 & 1.3003 & 0.1975 \\ \text { OpWin\% } & 11.3453 & 6.4617 & 1.7558 & 0.0833 \\ \text { Weekend } & 367.5377 & 2786.2639 & 0.1319 & 0.8954 \\ \text { Promotion } & 6927.8820 & 2784.3442 & 2.4882 & 0.0151 \\ \hline \end{array} The coefficient of multiple determination ( R 2 j) of each of the 5 predictors with all the other remaining predictors are, respectively, 0.2675, 0.3101, 0.1038, 0.7325, and 0.7308. -Referring to Table 15-9, which of the following assumptions is most likely violated based on the residual plot for WIN%? A) normality of errors B) linearity C) equal variance D) none of the above$

The coefficient of multiple determination ( R ²_j) of each of the 5 predictors with all the other remaining predictors are, respectively, 0.2675, 0.3101, 0.1038, 0.7325, and 0.7308.

-Referring to Table 15-9, which of the following assumptions is most likely violated based on the residual plot for WIN%?

Definitions:

Specific Customers

This refers to identified individual or business customers with unique needs or characteristics that a company targets or serves.

Direct Write-off Method

An accounting method where uncollectible debts are written off as an expense only when they are confirmed to be uncollectible.

Materiality Constraint

An accounting principle that allows for ignoring minor discrepancies that would not affect the decision-making process of users of financial statements.

Uncollectible Account Receivable

An accounting term for receivables from sales that are considered not collectible, resulting in a bad debt expense.