EBK BUSINESS STATISTICS
7th Edition
ISBN: 8220102743984
Author: STEPHAN
Publisher: PEARSON
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Find the degrees of freedom in a regression model with an intercept term that has 88 observations and 7 explanatory variables.
A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three
ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below.
Click the icon to view the table of summated ratings and cost per person.
.....
a. Construct a scatter plot. Choose the correct graph below.
A.
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ACost ($)
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ACost ($)
90-
ACost ($)
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Rating
Rating
Rating
b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,.
bo = and b,
(Round to two decimal places as needed.)
c. Interpret the meaning of the Y-intercept, bo, and the slope, b,. Choose the correct answer below.
O A.…
A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiled the
accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below.
Click the icon to view the table of summated ratings and cost per person.
a. Construct a scatter plot. Choose the correct graph below.
O A.
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OC.
OD.
ACost ($)
90-
Q
A Cost ($)
904
A Cost ($)
90-
ACost ($)
90-
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0-
0-
0-
90
Rating
90
Rating
90
90
Rating
Rating
Summated ratings and cost per
person
b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b,.
bo =D and b, =O
(Round to two decimal places as needed.)
Summated Rating Cost ($ per person)|9
c. Interpret the…
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- Does Table 2 represent a linear function? If so, finda linear equation that models the data.arrow_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardFind the mean hourly cost when the cell phone described above is used for 240 minutes.arrow_forward
- A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below. Click the icon to view the table of summated ratings and cost per person. a. Construct a scatter plot. Choose the correct graph below. O A. 90+ 0 0 Cost ($) The M Rating 90 Q O B. A Cost (5) 90+ 0 H +4 Alpe Rating 90 Q b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b₁. bo= and b₁ = (Round to two decimal places as needed.) C O C. 90+ 0- Cost (S) HA Rating 90 Q Summated Ratings and Cost Per Person Summated Rating Cost ($ per person) 40 48 60 61 42 40 43 55 67 69…arrow_forwardResearchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…arrow_forwardResearchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 104.004228 2 37 8.49446952 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 Interpret the slope associated with mother's height. t P>|t| 4.46 0.000 с 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Intervall 9567683 .4804261 34.92886arrow_forward
- Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight ), and height of the father ( fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 522.303829 2 104.004228 8.49446952 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 Calculate the test statistic that is labeled "C" in the output. t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, Prob > F 37) = R-squared Adj R-squared = Root MSE 40 12.24 0.0001 = 0.3983 0.3657 2.9145 .3591375 -.0797093 -15.32021 = [95% Conf. Intervall .9567683 .4804261…arrow_forwardyou take a bar of soap and weigh it after each shower. will the regression line be positive or negativearrow_forwardA marketing consultant created a linear regression model to predict the number of units sold by a client based on the amount of money spent on marketing by the client. Which of the following is the best graphic to use to evaluate the appropriateness of the model?arrow_forward
- After interviewing salespersons at Harley Davidson dealerships, a researcher has created a linear regression line to explain the relationship between a Harley Davidson motorcycle's age (x) and price (y). The regression has an = 87.7%. Write a sentence summarizing what says about this regression. The age of the motorcycle explains 12.3% of the variation in price. The age of the motorcycle explains 9.36% of the variation in price. The age of the motorcycle explains 87.7% of the variation in price. The price of the motorcycle explains 12.3% of the variation in age. The price of the motorcycle explains 87.7% of the variation in age.arrow_forwardProfessional basketball has truly become a sport that generates interest among fans around the world. More and more players come from outside the United States to play in the National Basketball Association (NBA). You want to develop a regression model to predict the number of wins achieved by each NBA team, based on field goal (shots made) percentage and three-point field goal percentage. The data are stored in NBA.xlsx . TEAM Wins Field Goal % Three-Point Field Goal % Points Per Game Rebound Freedraw Turnover Houston Rockets 65 46 36.2 112.4 43.5 19.6 13.8 Toronto Raptors 59 47.2 35.8 111.7 44 17.3 13.4 Golden State Warriors 58 50.3 39.1 113.5 43.5 16.6 15.4 Boston Celtics 55 45 37.7 104 44.5 16 14 Philadelphia 76ers 52 47.2 36.9 109.8 47.4 17.1 16.5 Cleveland Cavaliers 50 47.6 37.2 110.9 42.1 18.1 13.7 Portland Trail Blazers 49 45.2 36.6 105.6 45.5 16.7 13.5 Indiana Pacers 48 47.2 36.9 105.6 42.3 14.9 13.3 New Orleans Pelicans 48 48.3 36.2 111.7 44.3 16.1 14.9…arrow_forwardProfessional basketball has truly become a sport that generates interest among fans around the world. More and more players come from outside the United States to play in the National Basketball Association (NBA). You want to develop a regression model to predict the number of wins achieved by each NBA team, based on field goal (shots made) percentage and three-point field goal percentage. The data are stored in NBA.xlsx . TEAM Wins Field Goal % Three-Point Field Goal % Points Per Game Rebound Freedraw Turnover Houston Rockets 65 46 36.2 112.4 43.5 19.6 13.8 Toronto Raptors 59 47.2 35.8 111.7 44 17.3 13.4 Golden State Warriors 58 50.3 39.1 113.5 43.5 16.6 15.4 Boston Celtics 55 45 37.7 104 44.5 16 14 Philadelphia 76ers 52 47.2 36.9 109.8 47.4 17.1 16.5 Cleveland Cavaliers 50 47.6 37.2 110.9 42.1 18.1 13.7 Portland Trail Blazers 49 45.2 36.6 105.6 45.5 16.7 13.5 Indiana Pacers 48 47.2 36.9 105.6 42.3 14.9 13.3 New Orleans Pelicans 48 48.3 36.2 111.7 44.3 16.1 14.9…arrow_forward
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