Statistical Analysis: An Interdisciplinary Introduction to Univariate & Multivariate MethodsThis 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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Page 125
... binary population in question . This could be done by subjecting the 0 and 1 binary data to the definitional formula for a population standard deviation , but there is an easier approach , by virtue of the special characteristic of a binary ...
... binary population in question . This could be done by subjecting the 0 and 1 binary data to the definitional formula for a population standard deviation , but there is an easier approach , by virtue of the special characteristic of a binary ...
Page 129
... population . That the normal distribution should find application in a situation based on a discrete binary variable further attests to its pervasive importance . Standard error of the number of successes . In the preceding discussion ...
... population . That the normal distribution should find application in a situation based on a discrete binary variable further attests to its pervasive importance . Standard error of the number of successes . In the preceding discussion ...
Page 518
... binary population yields a proportion p = .59 . Test the hypothesis that the population proportion is really p = .50 , using the a = .05 significance level . 26. A sample of n = 1500 from a binary population yields a proportion of p ...
... binary population yields a proportion p = .59 . Test the hypothesis that the population proportion is really p = .50 , using the a = .05 significance level . 26. A sample of n = 1500 from a binary population yields a proportion of p ...
Contents
THE NATURE OF STATISTICAL ANALYSIS 1 Introduction | 3 |
Data | 4 |
Objectives of statistical analysis | 5 |
Copyright | |
212 other sections not shown
Common terms and phrases
alternative analysis of variance application associated average beta coefficients blocks calculate cell chapter cluster concomitant variable confidence interval consider correlation analysis correlation coefficient covariance criterion variable degrees of freedom determine discriminant function equal example expected experimental variable F ratio factor analysis given H₁ hours of instruction identify independent index numbers individual inter-object interaction interested interpretation interval estimate linear loading matrix measure median multiple correlation multivariate normal distribution null hypothesis number of observations number of variables obtained orthogonal contrasts P₁ package color pair population mean prediction predictor variables probability problem procedure random sample random variables regression analysis regression equation regression line reject relationship represent respective resulting sample mean sample statistics sampling distribution shelf space significance level simple outcomes spice sales squared deviations standard deviation standard error sum of squared technique test scores textbook tion treatment groups various x₁