Multivariate Statistical Analysis: A Conceptual IntroductionThis 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 32
... central tendency , or location as it is also called ; that is , at which value do the observations " tend to center , " or equivalently , where along the values of the variable in question are the observations clustered or " located ...
... central tendency , or location as it is also called ; that is , at which value do the observations " tend to center , " or equivalently , where along the values of the variable in question are the observations clustered or " located ...
Page 33
... central tendency is the ease with which it can be obtained , and its common sense interpretation . However , its limited mathematical properties make it less than the ideal measure of central tendency for more advanced analyses . The ...
... central tendency is the ease with which it can be obtained , and its common sense interpretation . However , its limited mathematical properties make it less than the ideal measure of central tendency for more advanced analyses . The ...
Page 41
... central tendency , or location , but differ markedly in their variation ; those in part b differ in central tendency , but have the same variation ; while those shown in part c differ both in variation and central tendency . So just as ...
... central tendency , or location , but differ markedly in their variation ; those in part b differ in central tendency , but have the same variation ; while those shown in part c differ both in variation and central tendency . So just as ...
Contents
FUNDAMENTAL CONCEPTS 1 Introduction | 1 |
Objects variables and scales | 8 |
Frequency distributions | 21 |
Copyright | |
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alternative application approach associated average calculate chapter characteristics cluster color concept consider correlation coefficient criterion variable defined degrees of freedom depending derived determine dimensions discriminant discriminant function discussion distance distribution effects equal equation error estimate example experiment experimental fact factor analysis Figure frequency given groups hypothesis identify important independent individual input instance inter-object interest interpretation interval less levels loading matrix mean measured median multiple named nature objects observations obtained occur original outcomes package pair particular performance population possible prediction predictor variables present probability problem procedure random ratio referred regression analysis relationship relative represent respective sample mean sampling distribution scale scores shape shown shows similarity simple situations space square standard deviation statistical Table technique true understand values variance variation various weights