Study the following Minitab output from a regression analysis to predict y from x.
a. What is the equation of the regression model?
b. What is the meaning of the coefficient of x?
c. What is the result of the test of the slope of the regression model? Let α = 0.10. Why is the t ratio negative?
d. Comment on r^2 and the standard error of the estimate.
e. Comment on the relationship of the F value to the t ratio for x.
f. The correlation coefficient for these two variables is 0.9482. Is this result surprising to you? Why or why not?
Regression Analysis: Y versus X
The regression equation is Y = 1.5698 + 0.04070X
Predictor Coef SE Coef T P
Constant 1.5698 0.3381 4.64 0.001
X 0.04070 0.004312 9.44 0.000
S = 0.17
R-Sq = 89.9%
R-Sq(adj) = 88.9%
Analysis of Variance
Source DF SS MS F P
Regression 1 2.7980 2.7980 89.09 0.000
Residual Error 10 0.3141 0.0314
Total 11 3.1121
a. The regression equation is: ŷ = 1.5698 + 0.04070x
b. For every unit of increase in the value of x, the predicted value of y will increase by 0.04070.
c. The t ratio for the slope is 9.44 with an associated p-value of 0.000.
d. r^2 is 89.9% of the variability of y is accounted for by x. This is proportion of predictability. The standard error of the estimate is 0.17. This is interpreted in light of the data and the magnitude of the data.
e. The F value which tests the overall predictability of the model is 89.09. For simple regression analysis, this equals the value of t ratio for x.
f. The correlation coefficient is not a surprise because the slope of the regression line is also indicating a relationship between x and y.