Introduction to Linear Regression Analysis. Douglas C. Montgomery

2357.90 2349.17 8.73 2256.70 2219.13 37.57 2165.20 2144.83 20.37 2399.55 2488.50 −88.95 1799.80 1698.98 80.82 2336.75 2265.58 71.17 1765.30 1810.44 −45.14 2053.50 1959.06 94.44 2414.40 2404.90 9.50 2200.50 2163.40 37.10 2654.20 2553.52 100.68 1753.70 1829.02 −75.32

      The least-squares fit is

      We may interpret the slope −37.15 as the average weekly decrease in propellant shear strength due to the age of the propellant. Since the lower limit of the x’s is near the origin, the intercept 2627.82 represents the shear strength in a batch of propellant immediately following manufacture. Table 2.2 displays the observed values yi, the fitted values , and the residuals.

      After obtaining the least-squares fit, a number of interesting questions come to mind:

      1 How well does this equation fit the data?

      2 Is the model likely to be useful as a predictor?

      3 Are any of the basic assumptions (such as constant variance and uncorrelated errors) violated, and if so, how serious is this?

All of these issues must be investigated before the model is finally adopted for use. As noted previously, the residuals play a key role in evaluating model adequacy. Residuals can be viewed as realizations of the model errors εi. Thus, to check the constant variance and uncorrelated errors assumption, we must ask ourselves if the residuals look like a random sample from a distribution with these properties. We return to these questions in Chapter 4, where the use of residuals in model adequacy checking is explored.

       TABLE 2.3 Minitab Regression Output for Example 2.1

Regression Analysis
The regression equation is
Strength = 2628- 37.2 Age
Predictor	Coef	StDev	T	P
Constant	2627.82	44.18	59.47	0.000
Age	-37.154	2.889	-12.86	0.000
S = 96.11	R-Sq = 90.2%	R-Sq(adj) = 89.6%
Analysis of Variance
Source	DF	SS	MS	F	P
Regression	1	1527483	1527483	165.38	0.000
Error	18	166255	9236
Total	19	1693738

       Computer Output

      Computer software packages are used extensively in fitting regression models. Regression routines are found in both network and PC-based statistical software, as well as in many popular spreadsheet packages. Table 2.3 presents the output from Minitab, a widely used PC-based statistics package, for the rocket propellant data in