In fitting a least squares line to 10 data points, the following quantities were computed:

SSxx= 32 SSyy= 26 SSxy= 28

Mean ofXs= 3 Mean ofYs= 4

The calculated regression equation is Y = 1.375 + 0.875X

SSE is equal to, and therefore the standarderror of the model is equal to

The mean value Y whereXp= 4 is equal to.

The 95% confidence interval for the mean value of Y whereXp=4 shows a lower bound ofand an upper bound of. This interval was computed using the appropriate multiplier t equal to, and the calculated standard error of the estimated mean value of Y equal to.

Moreover, knowing that the calculated coefficient of correlation is 0.971, we can say that approximatelypercent of the variability in Y is explain by the use of the predictor in the prediction model.