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Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Multivariate Data Reduction and Discrimination with SAS Software
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Khattree, Ravindra/ Naik, Dayanand N. 作者
Sas Inst
譯者
2000-5 出版日期
574 頁數
$ 76.78 價格
Pap
叢書系列
9781580256964 圖書編碼

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 圖書標籤: methodology  SAS   

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發表於2024-07-07

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 用戶評價

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居然又是第一個biu~factor analysis model選擇講的比step by step好~

評分☆☆☆☆☆

居然又是第一個biu~factor analysis model選擇講的比step by step好~

評分☆☆☆☆☆

居然又是第一個biu~factor analysis model選擇講的比step by step好~

評分☆☆☆☆☆

居然又是第一個biu~factor analysis model選擇講的比step by step好~

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 著者簡介

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 著者簡介

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Multivariate Data Reduction and Discrimination with SAS Software 在線電子書 圖書描述

Multivariate data commonly encountered in a variety of disciplines is easy to understand with the approaches and methods described in this book. The conceptual developments, theory, methods, and subsequent data analyses are presented systematically and in an integrated manner. The data analysis is performed using many multivariate analysis components available in SAS software. Illustrations are provided using an ample number of real data sets drawn from a variety of fields, and special care is taken to explain the SAS codes and the interpretation of corresponding outputs. As a companion volume to the authors' previous book, Applied Multivariate Analysis with SAS Software, which discusses multivariate normality-based analyses, this book covers topics where, for the most part, assuming multivariate normality (or any other distributional assumption) is not crucial. Since the techniques discussed in this book also form the foundation of data mining methodology, the book will be of interest to data mining practitioners.

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