Analysis
Regression Analysis: SCALL versus Q The regression equation is S CALL = 79.0 + 0.114 Q Predictor Coef SE Coef T P Constant 78.968 4.401 17.95 0.000Q 0.1145 0.1570 0.73 0.477S = 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 0.477Residual Error 16 3087.3 193.0Total 17 3189.8Unusual Observations Obs Q SCALL Fit SE Fit Residual St Resid6 91.0 97.00 89.38 11.81 7.62 1.04 X8 11.0 118.00 80.23 3.49 37.77 2.81RR 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?