Suppose systolic blood pressure, birth weight (oz), and age (days) are measured for 16 infants and the data are shown in the following table
|
ID
|
Birth weight (oz) ()
|
Age in days ()
|
SBP mmHg (y)
|
|
1
|
135
|
3
|
89
|
|
2
|
120
|
4
|
90
|
|
3
|
100
|
3
|
83
|
|
4
|
105
|
2
|
77
|
|
5
|
130
|
4
|
92
|
|
6
|
125
|
5
|
98
|
|
7
|
125
|
2
|
82
|
|
8
|
105
|
3
|
85
|
|
9
|
120
|
5
|
96
|
|
10
|
90
|
4
|
95
|
|
11
|
120
|
2
|
80
|
|
12
|
95
|
3
|
79
|
|
13
|
120
|
3
|
86
|
|
14
|
150
|
4
|
97
|
|
15
|
160
|
3
|
92
|
|
16
|
125
|
3
|
88
|
- Draw a scatter plot
- Fit a multiple regression equation.
- Calculate the predicted average systolic blood pressure of a baby with birth weight 8 128 oz measured at 3 days of life.
- Test the hypotheses that and at 5% level of significance.
- Construct 95% C.I's for and and comment on regression.
- Carry out an analysis of variance and test the significance of the regression using.
- Compute the coefficient of multiple determinations and multiple correlation coefficients for the above example.
- Carry out a residual analysis for the adequacy of the model estimated. Does any observation look like it could be influential? in its effect on the regression line?
- If yes, identify the influential points and reanalyze, excluding the influential observation. What have you noticed?