I have given all needed to answer, I am just unsure of the answer. I only need t
ID: 3318522 • Letter: I
Question
I have given all needed to answer, I am just unsure of the answer. I only need the number 44 answered please. Thanks in advance.
Blood pressure is an important health variable to keep track of. However, the ability to measure blood pressure requires skill or expensive equipment. If we could accurately predict blood pressure using other variables that are easier to measure we could track health in an inexpensive way. Based on some previous research, I decide to see if I could use an individual’s height, arm circumference, and age to predict their blood pressure. Here is the data collected, now I need you to run the statistics for me.
ID
Blood Pressure (mmHg)
Height (cm)
Arm Circumference (cm)
Age (years)
1
113
167.0
37.0
26
2
117
193.5
36.4
25
3
119
167.3
31.8
24
4
94
150.1
31.0
27
5
114
185.0
40.4
25
6
107
166.0
30.4
21
7
114
168.7
42.2
25
8
97
181.5
44.0
23
9
113
175.9
29.5
20
10
114
174.0
35.0
25
11
94
152.8
39.1
23
12
130
163.0
36.1
23
13
114
155.9
25.5
26
14
99
165.7
32.5
19
15
118
162.3
27.7
22
16
106
176.5
30.0
23
17
96
166.0
28.0
32
18
121
162.1
28.2
20
19
104
156.6
26.5
19
20
122
158.9
41.7
22
Which test should you run?
Z-test
Chi-square goodness of fit test
Multiple Regression
Correlation
Chi-square test of independence
Does the regression model significantly improve the ability to predict blood pressure over just guessing the average value?
Yes
No
Copy and paste the table that contains the information you used to answer question 42.
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
138.372
3
46.124
.397
.757b
Residual
1859.828
16
116.239
Total
1998.200
19
a. Dependent Variable: BP
b. Predictors: (Constant), Arm, Age, Height
44. What percentage of variance in blood pressure is explained by the regression model?
ID
Blood Pressure (mmHg)
Height (cm)
Arm Circumference (cm)
Age (years)
1
113
167.0
37.0
26
2
117
193.5
36.4
25
3
119
167.3
31.8
24
4
94
150.1
31.0
27
5
114
185.0
40.4
25
6
107
166.0
30.4
21
7
114
168.7
42.2
25
8
97
181.5
44.0
23
9
113
175.9
29.5
20
10
114
174.0
35.0
25
11
94
152.8
39.1
23
12
130
163.0
36.1
23
13
114
155.9
25.5
26
14
99
165.7
32.5
19
15
118
162.3
27.7
22
16
106
176.5
30.0
23
17
96
166.0
28.0
32
18
121
162.1
28.2
20
19
104
156.6
26.5
19
20
122
158.9
41.7
22
Explanation / Answer
a) we use Multiple regression model
No regression model significantly improve the ability to predict blood pressure.
from the table
F cal=0.397
and Pvalue=0.757 >0.05, hence model is not significant.
44) R square=SSR/SST= 0.069=7%
7 % of variance in blood pressure is explained by the regression model