Statistical analysis: an interdisciplinary introduction to univariate & multivariate methods
This classic book provides the much needed conceptual explanations of advanced computer-based multivariate data analysis techniques: correlation and regression analysis, factor analysis, discrimination analysis, cluster analysis, multi-dimensional scaling, perceptual mapping, and more. It closes the gap between spiraling technology and its intelligent application, fulfilling the potential of both.
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THE NATURE OF STATISTICAL ANALYSIS
Objectives of statistical analysis
203 other sections not shown
analysis of variance approximate average binary population binomial calculate cell chapter cluster concomitant variable confidence interval contingency table correlation analysis correlation coefficient covariance criterion variable degrees of freedom determine discriminant analysis discriminant function equal error variance example expected experimental variable F ratio factor analysis forecast frequency distribution given identify independent index numbers individual inter-object interpretation interval estimate loading matrix measured median multiple correlation multivariate normal distribution null hypothesis number of variables objects obtained package color pairs parent population payoff values population mean population parameter preceding prediction predictor variables problem procedure random sample random variables regression analysis regression equation regression line relationship represent respective resulting sample mean sample sizes sample space sample statistics sampling distribution scale shelf space simple outcomes spice sales squared deviations standard deviation standard error statistical analysis sum of squared technique tion treatment groups type II error variance estimate variation various weights