The ANCOVA output in .NET Integration UPC-A in .NET The ANCOVA output

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30.6 The ANCOVA output using barcode writer for .net control to generate, create upc-a supplement 2 image in .net applications. UCC.EAN - 128 The output of the omnibus A .net vs 2010 GS1 - 12 NCOVA is shown in Figure 30.14.

It is structured in the same way as a one-way between-subjects ANOVA. We have discussed the structure of this output in Section 23.6 and will not repeat it.

Our interest is in the lower summary table in which the effects of the covariate and the independent variable. One-Way Between-Subjects Analysis of Covariance The Model contains the effe UPC-A Supplement 5 for .NET cts of both the covariate and the independent variable. The effects of the covariate and the independent variable are separately evaluated in this summary table.

. Figure 30.14. The results of the omnibus analysis. are separately evaluated. A .net vs 2010 UPCA s we can see, the covariate gardening exp cov was statistically signi cant.

Using the corrected total sum of squares shown in the upper table as our base, its eta-square value can be calculated as 1115.082731/1538.75, or 2 = .

725. We can therefore assert that prior gardening experience and knowledge was quite in uential in how well the trees fared under the attention of the young campers. Of primary interest was the independent variable of watering technique.

As we can see from the lower summary table, it too was statistically signi cant. The eta-square value associated with this variable is 408.167269/1538.

75, or 2 = .265. This suggests that the technique used for watering the trees, when we statistically control for or equate the gardening experience and knowledge of the children, was a relatively strong factor in how much growth was seen in the trees.

The results of the pairwise comparisons of the group means are shown in Figure 30.15. The upper table displays the least squares means for the number of inches the trees grew over the summer, adjusted for the gardening experience covariate.

Recall that the watering techniques coded 1, 2, and 3 represented hose watering, drip watering, and deep watering, respectively. The lower table in Figure 30.15 provides the results of the pairwise comparisons after the Tukey Kramer strategy is used to maintain a familywise error rate at .

05. As we can see from the table, the only signi cant difference was between the means. Advanced ANOVA Techniques Watering techniques coded a Visual Studio .NET GTIN - 12 s 1 (hose watering) and 3 (deep watering) are the only two groups whose means differ significantly..

Figure 30.15. The results of the Tukey Kramer adjustment for multiple comparisons. for the watering techniques visual .net UPC-A coded as 1 and 3. On the basis of the adjusted means, we may therefore conclude that, when we statistically control for gardening experience, deep watering is more effective than hose watering but is not signi cantly more effective than drip watering.

. 31 One-Way Between-Subjects Multivariate Analysis of Variance 31.1 Overview The ANOVA designs discussed in s 23 through 26 examined the effect of one or more independent variables on a single dependent variable. Because such designs focus on a single dependent variable, they are labeled as univariate ANOVA designs. The present chapter addresses designs in which two or more dependent variables are analyzed simultaneously; such designs are known as multivariate analysis of variance (MANOVA) designs.

We limit our discussion to the simplest illustration of such a design: a two-group one-way between-subjects design. More information about MANOVA can be found in Meyers et al. (2006), Stevens (2002), and Warner (2008).

. 31.2 Univariate and multivariate ANOVA Univariate ANOVA designs ar e extremely useful but their focus is on a single outcome measure. For example, in evaluating a new curriculum designed to teach children to read more quickly, a natural variable to measure is reading speed. But at the same time that the reading speed of the children was improving (assuming a successful curriculum), there might potentially be other variables changing in synchrony, such as reading comprehension, enhanced levels of self-con dence, and feelings of mastery.

Perhaps improvements in other academic subjects might be observed as well. All of these related (correlated) effects could serve as potential dependent variables. To focus only on one of these variables, reading speed for example, narrows the focus of the study perhaps to an unnecessary extent.

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