By Yasui Y.

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Extra resources for A data-analytic strategy for protein biomarker discovery profiling of high-dimensional proteomic dat

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Measures of model fit and descriptive statistics for the dependent and independent variables are also available. Additionally, you can request estimated marginal means for levels of model factors and factor interactions. Data. The dependent variable is quantitative. Factors are categorical. Covariates are quantitative variables that are related to the dependent variable. Subpopulation variables can be string or numeric but should be categorical. Assumptions. The cases in the data file represent a sample from a complex design that should be analyzed according to the specifications in the file selected in the Complex Samples Plan dialog box.

These contrasts are often used to estimate polynomial trends. Reference Category. The simple and deviation contrasts require a reference category, or factor level against which the others are compared. Complex Samples General Linear Model Save Figure 9-6 General Linear Model Save dialog box Save Variables. This group allows you to save the model predicted values and residuals as new variables in the working file. Export Model as SPSS data. Writes an SPSS data file containing a covariance (or correlation, if selected) matrix of the parameter estimates in the model.

The null hypothesis for each test is that the value of the coefficient is 0. 47 Complex Samples General Linear Model „ Covariances of parameter estimates. Displays an estimate of the covariance matrix for the model coefficients. „ Correlations of parameter estimates. Displays an estimate of the correlation matrix for the model coefficients. „ Design effect. The ratio of the variance of the estimate to the variance obtained by assuming that the sample is a simple random sample. This is a measure of the effect of specifying a complex design, where values further from 1 indicate greater effects.

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