In a study of the relationship between creatinine excretion (dependent variable)
ID: 3325950 • Letter: I
Question
In a study of the relationship between creatinine excretion (dependent variable), height, and weight, the data shown in the file were collected on 20 infant males. Run multiple regression analysis.
Infant
CreatinineExcretion_mgday_Y
Weight_kg_X1
Height_cm_X2
1
100
9
72
2
115
10
76
3
52
6
59
4
85
8
68
5
135
10
60
6
58
5
58
7
90
8
70
8
60
7
65
9
45
4
54
10
125
11
83
11
86
7
64
12
80
7
66
13
65
6
61
14
95
8
66
15
25
5
57
16
125
11
81
17
40
5
59
18
95
9
71
19
70
6
62
20
120
10
75
14.1 Copy and paste the three output tables: model summary, ANOVA, and coefficients
14.2 What is the multiple correlation coefficient?
14.3 What is the coefficient of determination? What does it denote?
14.4 Is the overall regression model statistically significant? Provide an evidence to support
your answer.
14.5 What are the beta coefficients of the two independent variables?
14.6 Interpret the meaning of the two beta coefficients. In other words, what kind of
influences the two independent variables have on weight?
14.7 Write the equation for the regression line.
14.8 Let weight (X1) = 10 and height (X2) = 60 and find the predicted value of Y.
Infant
CreatinineExcretion_mgday_Y
Weight_kg_X1
Height_cm_X2
1
100
9
72
2
115
10
76
3
52
6
59
4
85
8
68
5
135
10
60
6
58
5
58
7
90
8
70
8
60
7
65
9
45
4
54
10
125
11
83
11
86
7
64
12
80
7
66
13
65
6
61
14
95
8
66
15
25
5
57
16
125
11
81
17
40
5
59
18
95
9
71
19
70
6
62
20
120
10
75
Explanation / Answer
14.1 Summary Table
Anova table:
Regression coefficients :
14.2
Multiple R 0.957208678
14.3
R Square 0.916248454
14.4
Significant value is less than alpha 0.05, so we reject H0
Thus we conclude that regression model is statistically significant
14.5
Coefficients
Intercept 23.19345004
X1 17.55381795
X2 -1.104784724
14.6
P-value of X1 is less than alpha 0.05, so it is significant
P-value of X2 is greater than alpha 0.5, so it is not significant
14.7
Y= 23.19345004 + 17.55381795X1 -1.104784724X2
14.8. Given X1 = 10 and X2 = 60
The predicted value of Y is
Y - hat = 23.19345004 + 17.55381795(10) -1.104784724(10)
= 187.6837823
SUMMARY OUTPUT Regression Statistics Multiple R 0.957208678 R Square 0.916248454 Adjusted R Square 0.906395331 Standard Error 9.556135734 Observations 20Related Questions
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