Suppose that you fit the model
E(y) = β0 + β1x + β2x2
to 20 data points and obtained the accompanying MINITAB printout.
MINITAB output for Exercise 13.5
Regression Analysis: y versus x, x-sq
The regression equation is
y = 10.6 + 4.44 x - 0.648 x-sq

a. What type of model have you chosen to fit the data?
b. How well does the model fit the data? Explain.
c. Do the data provide sufficient evidence to indicate that the model contributes information for the prediction of y? Use the p-value approach.

Transcribed Image Text:

Predictor Constant 2x X-sq Coef 10.5638 4.4366 SE Coef 0.6951 15.20 0.000 8.61 0.000 -0.64754 0.07988 8.11 0.000 0.5150 1.191 R-Sq 81.5% R-Sq (adj ) #79.3% Analysis of Variance Source Regression DE MS 2 106.072 53.036 37.37 0.000 1.419 Residual Error Total 17 24.128 19 130.200