Statistics > EXAM > Summary 1ZM31 Multivariate Data Analysis by Hair [Distinction] Questions and Answers 2023 (All)

Summary 1ZM31 Multivariate Data Analysis by Hair [Distinction] Questions and Answers 2023

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Bootstrapping - -An approach to validating a multivariate model by drawing a large number of sub- samples and estimating models for each subsample. Estimates from all the subsamples are then com- bine... d, providing not only the "best" estimated coefficients (e.g., means of each estimated coefficient across all the subsample models), but their expected variability and thus their likelihood of differing from zero; that is, are the estimated coefficients statistically different from zero or not? This approach does not rely on statistical assumptions about the population to assess statistical significance, but instead makes its assessment based solely on the sample data. -Composite measure - -See summated scales. -Dependence technique - -Classification of statistical techniques distinguished by having a variable or set of variables identified as the dependent variable(s) and the remaining variables as independent. The objective is prediction of the dependent variable(s) by the independent variable(s). An example is regression analysis. -Dependent variable - -Presumed effect of, or response to, a change in the independent variable(s). Dummy variable Nonmetrically measured variable transformed into a metric variable by assign- ing a 1 or a 0 to a subject, depending on whether it possesses a particular characteristic. -Effect size - -Estimate of the degree to which the phenomenon being studied (e.g., correlation or difference in means) exists in the population. -Independent variable - -Presumed cause of any change in the dependent variable. -Indicator - -Single variable used in conjunction with one or more other variables to form a composite measure. -Interdependence technique - -Classification of [Show More]

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