we have given data ,
|
x |
y |
|
37.2 |
26.9 |
|
80.9 |
37.6 |
|
83.7 |
45 |
|
55 |
31.2 |
|
46.3 |
29.1 |
|
82 |
44.1 |
|
71.6 |
36.1 |
|
54.7 |
29.3 |
|
56.4 |
33.8 |
Regression
Analysis: y versus x
Analysis of Variance
Source DF Seq SS Contribution Adj SS Adj MS F-Value
P-Value
Regression 1 298.33 87.96% 298.33 298.332 51.14 0.000
x 1 298.33 87.96% 298.33 298.332 51.14 0.000
Error 7 40.84 12.04% 40.84 5.834
Total 8 339.17 100.00%
Model Summary
S R-sq R-sq(adj) PRESS R-sq(pred)
2.41532 87.96% 86.24% 71.9728 78.78%
Coefficients
Term Coef SE Coef 95% CI T-Value P-Value VIF
Constant 12.09 3.27 ( 4.35, 19.83) 3.69 0.008
x 0.3598 0.0503 (0.2408, 0.4787) 7.15 0.000 1.00
Regression Equation
y = 12.09 + 0.3598 x
Now ,if x= 45.1 then
y =12.09+0.3598*45.1
y=28.3
So we get predicted response value =28.3
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