percentiles. in Java Add European Article Number 13 in Java percentiles.

percentiles. using j2ee toincoporate ean-13 supplement 5 in asp.net web,windows application Microsoft .NET Micro Framework 324 15 . Explore Plots Figure 15-4 Explore Plots dialog box Boxplots. These alternatives control the display of boxplots when you have more than one dependent variable. GTIN-13 for Java Factor levels together generates a separate display for each dependent variable. Within a display, boxplots are shown for each of the groups defined by a factor variable.

Dependents together generates a separate display for each group defined by a factor variable. Within a display, boxplots are shown side by side for each dependent variable. This display is particularly useful when the different variables represent a single characteristic measured at different times.

. Descriptive. The Descriptive group allows you to choose stem-and-leaf plots and histograms. Normality plots with tests. Displays normal probability and detrended normal probability plots. The K j2ee ean13+2 olmogorov-Smirnov statistic, with a Lilliefors significance level for testing normality, is displayed. If non-integer weights are specified, the Shapiro-Wilk statistic is calculated when the weighted sample size lies between 3 and 50.

For no weights or integer weights, the statistic is calculated when the weighted sample size lies between 3 and 5000.. Spread vs. Level with Levene Test. Controls data transformation for spread-versus-level plots. For all spread-ve awt EAN13 rsus-level plots, the slope of the regression line and Levene s robust tests for homogeneity of variance are displayed. If you select a transformation,.

325 Explore Levene s tests are based j2ee EAN13 on the transformed data. If no factor variable is selected, spread-versus-level plots are not produced. Power estimation produces a plot of the natural logs of the interquartile ranges against the natural logs of the medians for all cells, as well as an estimate of the power transformation for achieving equal variances in the cells.

A spread-versus-level plot helps determine the power for a transformation to stabilize (make more equal) variances across groups. Transformed allows you to select one of the power alternatives, perhaps following the recommendation from power estimation, and produces plots of transformed data. The interquartile range and median of the transformed data are plotted.

Untransformed produces plots of the raw data. This is equivalent to a transformation with a power of 1..

Explore Power Transformations These are the power tran jdk EAN13 sformations for spread-versus-level plots. To transform data, you must select a power for the transformation. You can choose one of the following alternatives:.

Natural log. Natural log transformation. This is the default.

1/square root. For each data value, the reciprocal of the square root is calculated. Reciprocal.

The reciprocal of each data value is calculated. Square root. The square root of each data value is calculated.

Square. Each data value is squared. Cube.

Each data value is cubed.. Explore Options Figure 15-5 Explore Options dialog box Missing Values. Controls the treatment of missing values. 326 15 . Exclude cases listwise. Cases with missing values for any dependent or factor variable are excluded from all analyses. This is the default. Exclude cases pairwise. Cases with no missing values for variables in a group (cell) are included in t he analysis of that group. The case may have missing values for variables used in other groups..

Report values. Missing values for factor variables are treated as a separate category. All output is produced for this additional category. Frequency tables include categories for missing values.

Missing values for a factor variable are included but labeled as missing.. Crosstabs The Crosstabs procedure forms two-way and multiway tables and provides a variety of tests and measures of association for two-way tables. The structure of the table and whether categories are ordered determine what test or measure to use. Crosstabs statistics and measures of association are computed for two-way tables only.

If you specify a row, a column, and a layer factor (control variable), the Crosstabs procedure forms one panel of associated statistics and measures for each value of the layer factor (or a combination of values for two or more control variables). For example, if gender is a layer factor for a table of married (yes, no) against life (is life exciting, routine, or dull), the results for a two-way table for the females are computed separately from those for the males and printed as panels following one another..

Example. Are customers from small companies more likely to be profitable in sales of services (for example, t raining and consulting) than those from larger companies From a crosstabulation, you might learn that the majority of small companies (fewer than 500 employees) yield high service profits, while the majority of large companies (more than 2,500 employees) yield low service profits.. Statistics and measures ean13+5 for Java of association. Pearson chi-square, likelihood-ratio chi-square,. linear-by-linear associa tion test, Fisher s exact test, Yates corrected chi-square, Pearson s r, Spearman s rho, contingency coefficient, phi, Cram r s V, symmetric and asymmetric lambdas, Goodman and Kruskal s tau, uncertainty coefficient, gamma, Somers d, Kendall s tau-b, Kendall s tau-c, eta coefficient, Cohen s kappa, relative risk estimate, odds ratio, McNemar test, and Cochran s and Mantel-Haenszel statistics..
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