24 : 54 26 25: 73 49 A magazine publishes restaurant ratings for various locatio
ID: 3268291 • Letter: 2
Question
24 : 54 26
25: 73 49
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. b_0 = and b_1 = (Round to two decimal places as needed.) c. Interpret the meaning of the Y-intercept, b_0, and the slope, b_1. Choose the correct answer below. A. The Y-intercept, b_0, implies that if the summated rating of a restaurant is zero, the cost per person is b_0, in dollars. The slope, b_1, implies the average cost per person is b_1 dollars. B. The Y-intercept, b_0, implies that if the summated rating of a restaurant is zero, the cost per meal is equal to b_0, in dollars. The slope, b_1, implies that for each increase of 1 in the summated rating, the cost per person is expected to decrease by b_1, in dollars. C. A practical interpretation of the Y-intercept b_0 is not meaningful because no operating restaurant is likely to have a rating of zero. The slope b_1 implies that for each increase of 1 in the summated rating, the cost per person is expected to increase by the value of b_1, in dollars. D. The Y-intercept, b_0, implies that if the summated rating of a restaurant is zero, the cost per meal is equal to b_0, in dollars.Explanation / Answer
We copy the data in excel. There we go to Data Analysis and select the Regression. We input the data for x and y and we get the regression output where we can find the b0 and b1 values.
x
y
55
37
67
50
70
60
65
57
66
43
61
38
56
46
62
40
52
35
48
35
53
40
51
39
76
88
61
51
52
39
49
35
56
33
44
26
58
41
54
45
67
66
63
60
54
26
73
49
67
45
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.7861
R Square
0.6180
Adjusted R Square
0.6014
Standard Error
8.5322
Observations
25
ANOVA
df
SS
MS
F
Significance F
Regression
1
2708.603431
2708.603
37.20706
3.19598E-06
Residual
23
1674.356569
72.79811
Total
24
4382.96
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-31.03
12.57383009
-2.46764
0.021469
-57.03868639
-5.01678806
X Variable 1
1.28
0.210430725
6.099759
3.2E-06
0.848267525
1.71888576
Question b)
Answer:
bo = -31.03
b1 = 1.28
Question c)
Answer: Option C
x
y
55
37
67
50
70
60
65
57
66
43
61
38
56
46
62
40
52
35
48
35
53
40
51
39
76
88
61
51
52
39
49
35
56
33
44
26
58
41
54
45
67
66
63
60
54
26
73
49
67
45
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