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Predictive Statictics

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Descriptive statistis are used to summarize data under study. Some descriptive statistics summarize the distribution of attributes on a single variable; others summarize the associations between variables.

• Descriptive statistics summarizing the relationships between variables are called measures of association.

• Many measures of association are based on a proportionate reduction of error (PRE) model. This model is based on a comparison of 1. the numbers of errors we would make in attempting to guess the attributes of a given variable for each of the cases under study - if we knew nothing but the distribution of attributes on that variable - and 2. the number of errors we would make if we knew the joint distribution overall and were told for each case the attribute o one variable each time we were asked to guess the attribute of the other. These measures include lambda, which is appropriate for the analysis of two nominal variables; gamma, which is appropriate for the analysis of two ordinal variables; and Pearson's product-moment correlation, which is appropriate for the analysis of two interval or ratio variables.

• Regression analysis represents the relationships between variables in the form of equations, which can be used to predict the values of a dependent variable on the basis of values of one or more independent variables

• Regression equations are computed on the basis of a regression line: the geometric line representing, with the least amount of discrepancy, the actual location of points in a scattergram.

• Types of regression analysis include linear regression analysis, multiple regression analysis, partial regression analysis, and curvilinear regression analysis.

Inferential Statistics

• Inferential statistics are used to estimate the generalizability of findings arrived at through the analysis of a sampling to the larger

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