Multivariate Statistical Analysis: A Conceptual Introduction |
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Page 171
... Prediction y If the assumptions of the above regression model are met , we can be assured that the least squares method will yield a sample regression line , y ' = a + bx , which is an unbiased ... PREDICTION 171 Accuracy of prediction,
... Prediction y If the assumptions of the above regression model are met , we can be assured that the least squares method will yield a sample regression line , y ' = a + bx , which is an unbiased ... PREDICTION 171 Accuracy of prediction,
Page 174
... prediction errors , it is fortunate that as n becomes large , say greater than 100 , it can provide approximate confidence intervals , since the errors in the estimation of a and ẞ become small relative to s ... This will be apparent ...
... prediction errors , it is fortunate that as n becomes large , say greater than 100 , it can provide approximate confidence intervals , since the errors in the estimation of a and ẞ become small relative to s ... This will be apparent ...
Page 180
A Conceptual Introduction Sam Kash Kachigan. Prediction errors , y y ' Prediction errors , y - y ' ( a ) Linear model plausible X ( c ) Curvilinear relationship between errors and x ; linear model inappropriate X Prediction errors , y ...
A Conceptual Introduction Sam Kash Kachigan. Prediction errors , y y ' Prediction errors , y - y ' ( a ) Linear model plausible X ( c ) Curvilinear relationship between errors and x ; linear model inappropriate X Prediction errors , y ...
Contents
FUNDAMENTAL CONCEPTS 1 Introduction | 1 |
Objects variables and scales | 8 |
Frequency distributions | 21 |
Copyright | |
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Common terms and phrases
alternative analysis of variance application associated average calculated central tendency chapter cluster analysis composite outcomes concept confidence interval consider correlation analysis correlation coefficient criterion groups criterion variable cutoff score degrees of freedom determine dichotomous discriminant analysis discriminant function equal example experimental variable F ratio factor analysis factor loadings frequency distribution groups variance high loading hypothesis identify individual input variables inter-object similarity interaction interpretation loading on Factor measured median multiple correlation non-risks normal distribution number of observations number of variables occur package color pair population variance prediction random variables regression analysis regression equation regression line relationship represent respective rotation sample mean sample space sample statistics sampling distribution scale set of objects set of variables shelf space shown in Figure similarity matrix simple outcomes spice sales squared deviations standard deviation standard error statistical analysis Table technique test scores tion variance estimate variation