Regression Analysis: SCALL versus Q
The regression equation is
SCALL = 79.0 + 0.114 Q
Predictor Coef SE Coef T P
Constant 78.968 4.401 17.95 0.000
Q 0.1145 0.1570 0.73 0.477
S = 13.8908 R-Sq = 3.2% R-Sq(adj) = 0.0%
Analysis of Variance
Source DF SS MS F P
Regression 1 102.5 102.5 0.53 0.477
Residual Error 16 3087.3 193.0
Total 17 3189.8
Obs Q SCALL Fit SE Fit Residual St Resid
6 91.0 97.00 89.38 11.81 7.62 1.04 X
8 11.0 118.00 80.23 3.49 37.77 2.81R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
The value of r^2 squared is the proportion of total variation in the n observed values of y that is explained by the simple linear regression model. The closer the value is to r^2, the larger the proportion of the total variation that is explained by the model. In this case, r^2= 0.032 which is extremely low and suggests that there is little support for the model being a predictor of y in terms of x. Can we get a better model? Comments welcome by all!
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