A magazine publishes restaurant ratings for various locations around the world.
ID: 3268292 • Letter: A
Question
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. Predict the mean cost per person for a restaurant with a summated rating of 50. Y^_i = $ per person (Round to the nearest cent as needed.) What should you tell the owner of a group of restaurants in this geographical area about the relationship between the summated rating and the cost of a meal? A. As expected, the lower the summated rating of the restaurant, the higher the restaurant can charge per meal. B. As expected, the higher the summated rating of the restaurant, the higher the restaurant can charge per meal. C. As expected, the higher the summated rating of the restaurant, the less the restaurant can charge per meal. D. As expected, the lower the summated rating of the restaurant, the less the restaurant can charge per meal.Explanation / Answer
Answer:
The regression line : cost = -31.03+1.28* rating
d). Predicted mean cost =$ 33.15
B. As expected, the higher the summated rating of the restaurant, the higher the restaurant can charge per meal.
Regression Analysis
r²
0.618
n
25
r
0.786
k
1
Std. Error
8.532
Dep. Var.
cost
ANOVA table
Source
SS
df
MS
F
p-value
Regression
2,708.6034
1
2,708.6034
37.21
3.20E-06
Residual
1,674.3566
23
72.7981
Total
4,382.9600
24
Regression output
confidence interval
variables
coefficients
std. error
t (df=23)
p-value
95% lower
95% upper
Intercept
-31.0277
12.5738
-2.468
.0215
-57.0387
-5.0168
rating
1.2836
0.2104
6.100
3.20E-06
0.8483
1.7189
Predicted values for: cost
95% Confidence Interval
95% Prediction Interval
rating
Predicted
lower
upper
lower
upper
Leverage
50
33.151
27.813
38.490
14.711
51.591
0.091
Regression Analysis
r²
0.618
n
25
r
0.786
k
1
Std. Error
8.532
Dep. Var.
cost
ANOVA table
Source
SS
df
MS
F
p-value
Regression
2,708.6034
1
2,708.6034
37.21
3.20E-06
Residual
1,674.3566
23
72.7981
Total
4,382.9600
24
Regression output
confidence interval
variables
coefficients
std. error
t (df=23)
p-value
95% lower
95% upper
Intercept
-31.0277
12.5738
-2.468
.0215
-57.0387
-5.0168
rating
1.2836
0.2104
6.100
3.20E-06
0.8483
1.7189
Predicted values for: cost
95% Confidence Interval
95% Prediction Interval
rating
Predicted
lower
upper
lower
upper
Leverage
50
33.151
27.813
38.490
14.711
51.591
0.091
Related Questions
drjack9650@gmail.com
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.