The Unscrambler如何做PCA.ppt

  1. 1、本文档共34页,可阅读全部内容。
  2. 2、有哪些信誉好的足球投注网站(book118)网站文档一经付费(服务费),不意味着购买了该文档的版权,仅供个人/单位学习、研究之用,不得用于商业用途,未经授权,严禁复制、发行、汇编、翻译或者网络传播等,侵权必究。
  3. 3、本站所有内容均由合作方或网友上传,本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺!文档内容仅供研究参考,付费前请自行鉴别。如您付费,意味着您自己接受本站规则且自行承担风险,本站不退款、不进行额外附加服务;查看《如何避免下载的几个坑》。如果您已付费下载过本站文档,您可以点击 这里二次下载
  4. 4、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等,请点击“版权申诉”(推荐),也可以打举报电话:400-050-0827(电话支持时间:9:00-18:30)。
查看更多
The Unscrambler如何做PCA

PCA Principal Component Analysis This presentation covers the following Why PCA Principles behind PCA Interpretation of PCA plots in The Unscrambler Note: This presentation was made for versions of PowerPoint 2000 and earlier If you would like the version of this presentation for PowerPoint XP, please contact RanjitV@CAMO.com Principal Component Analysis (PCA) Why PCA? A typical data analysis situation 12 Jams samples were made from berries plucked in various cultivars and seasonal times. Several parameters (sensory measurements) were measured on each sample. Data set Raspberry Jams Sample comparison according to 1 variable: Redness Sample comparison according to 2 variables: Redness and colour Sample comparison according to 3 variables: Redness, colour and R. Smell Sample comparison according to all 12 variables: multivariate model (PCA) Sample comparison according to all 12 variables: multivariate model (PCA) Sample comparison according to all 12 variables: multivariate model (PCA) Multivariate Analysis Applies in any situation where several variables were measured Does not study the multiple variables separately; it utilizes all variables into 1 model Handles missing values Fully exploits the information contained in your data Principal Component Analysis (PCA) Principles behind PCA The principles of Principal Component Analysis (PCA) The principles of Principal Component Analysis (PCA) The principles of Principal Component Analysis (PCA) PCA- Scores Plot PCA- Scores Plot PCA- Scores Plot PCA- Scores Plot PCA – Loadings Plot PCA – Loadings Plot PCA – Loadings Plot PCA – Loadings Plot PCA – Loadings Plot PCA – Bi Plots PCA – Bi Plots Principal Component Analysis (PCA) Interpretation of Plots PCA Overview Loadings Plot (top right plot) Loadings Plot (top right plot) Scores Plot (top left plot) Scores Plot (top left plot) Bi-Plot Scores and Loadings on same plot For more details, please feel free to contact us Ranjit Viswanathan Manager – Sales (Asia Pacific) CA

文档评论(0)

153****9595 + 关注
实名认证
内容提供者

该用户很懒,什么也没介绍

1亿VIP精品文档

相关文档