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signi cantly for different independent variables. To detect multicolinearity, several methods have been developed. One method involves computing the pairwise correlations between explanatory variables. One rule of thumb suggested by some researchers is that mutlicolinearity may be a serious problem if any pairwise correlation is greater than 0.5 (Dielman, 1996). Multicolinearity was examined using this method and all pairwise correlation was below 0.5. The correlations between random variables are denoted by r and are calculated as: r = rx1x2 = S12 S11S22

Create Procedure prCnvDigit -- convert digit to word -- 8 -> eight @chrDigit char, @chvWord varchar(15) OUTPUT, @debug int = 0 As set nocount on Select @chvWord = Case @chrDigit when '1' Then 'one' when '2' Then 'two' when '3' Then 'three' when '4' Then 'four' when '5' Then 'five' when '6' Then 'six' when '7' Then 'seven' when '8' Then 'eight' when '9' Then 'nine' else '' end

(29.5)

as long as (B Ni A2)/( Ni)2 0.3. Substituting test data into Eq. (29.5) gives = 1.620(0.5) 342(27) 822 + 0.029 = 2.93 kpsi 272

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return go Alter Procedure prCnvDecade -- convert 20, 30, 40... @chrDecade varchar(2), @chvWord varchar(500) OUTPUT As set nocount on Select @chvWord = Case @chrDecade when '2' Then 'twenty' when '3' Then 'thirty' when '4' Then 'forty' when '5' Then 'fifty ' when '6' Then 'sixty' when '7' Then 'seventy' when '8' Then 'eighty' when '9' Then 'ninety' end return go Alter Procedure prCnvTeen -- convert numbers between 10 and 19 to word @chvNumber varchar(2), @chvWord varchar(15) OUTPUT As set nocount on Select @chvWord = Case @chvNumber when '10' Then 'ten' when '11' Then 'eleven' when '12' Then 'twelve' when '13' Then 'thirteen' when '14' Then 'fourteen' when '15' Then 'fifteen ' when '16' Then 'sixteen' when '17' Then 'seventeen' when '18' Then 'eighteen' when '19' Then 'nineteen' end

S11, S12, and S22 may be found in the (X X) matrix in the off diagonals. All correlations between independent variables were less than 0.001.

The result of the up-down test can be expressed as (Sf )107( , ) or (Sf )107(46.27, 2.93). Consult Refs. [29.3] and [29.4] for modification of the usual t-statistic method of placing a confidence interval on and Ref. [29.4] for placing a confidence interval on . A point estimate of the coefficient of variation is / = 2.93/46.27, or 0.063. Coefficients of variation larger than 0.1 have been observed in steels. One must examine the sources of tables that display a single value for an endurance strength to discover whether the mean or the smallest value in a sample is being reported. This can also reveal the coefficient of variation.

10:

The usual presentation of R. R. Moore testing results is on a plot of Sf (or Sf /Su) versus N, commonly on log-log coordinates because segments of the locus appear to be rectified. Figure 29.2 is a common example. Because of the dispersion in results, sometimes a 3 band is placed about the median locus or (preferably) the data points are shown as in Fig. 29.3. In any presentation of this sort, the only things that might be true are the observations. All lines or loci are curve fits of convenience, there being no theory to suggest a rational form. What will endure are the data and

This section discusses the results and ndings from the regression analysis conducted on the 20 waste groups. Included in this discussion are lists of the independent variables that are signi cant in predicting annual solid waste for each group, the full regression results (F-test, t-test, and R2-coef cient of determination), ANOVA, and correlation analysis. Table 16.3 and Fig. 16.5 display the signi cant variables that are

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Find the Source of the Error After you identify the minimum circumstances under which the error will occur, you can proceed to find the source of the error. If your code is properly structured and well written, this search should not be a difficult task. You can apply a variety of tools at this point:

FIGURE 29.2 Fatigue data on 2024-T3 aluminum alloy for narrow-band random loading, , and for constant-amplitude loading, . (Adapted with permission from Haugen [29.14], p. 339.)

TABLE 16.3 SIGNIFICANT VARIABLES THAT INFLUENCE SOLID WASTE FOR THE 20 WASTE GROUPS LANDFILL DISPOSAL COST ($/TON)

FIGURE 29.3 Statistical SN diagram for constant-amplitude and narrow-band random loading for a low-alloy steel. Note the absence of a knee in the random loading.

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