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輔大開放式課程  >  991多變量分析-李天行老師

課程名稱：多變量分析

 

授課教師：企業管理學系 李天行 老師

 

開課單位：日間部/企業管理學系 管理學研究所

 

授課時數：每週3小時，共18週

 

授課對象：研究所各年級學生

The purpose of this course is to give an introduction of the multivariate statistical analysis related techniques. As the support of statistical software and high speed computers, the focus of this course will be application oriented and software oriented. The students are expected to use the related skills in solving real world problems. The students are also required to use the SAS software in analyzing real world dataset.

 

/  學習目標

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學習目標

/  課程知識架構圖

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期中考 (筆試)：40%

 

課堂後測/期末考(筆試)：40%

 

個案分析報告撰寫：10%

 

課堂參與：10%

/  評量標準

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1.Applied Multivariate Statistical Analysis (6th Edition) by Johnson and Wichern
2.Multivariate Data Analysis with Readings (6th Edition) by Hair, Anderson, Tatham and Black

/  參考書目

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/  課程內容

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製作團隊: 教發中心；輔大影傳系 周鉅宏

 

課程                                 主題                                                                       教材連結

 

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影音    1    2    3

  • Review of Probability and Statistics

影音    1    2    3    4    5    6    7

  • Review of Linear Algebra

影音    1    2    3    4    5    6    7

  • Sample Geometry and Random Sampling

影音    1    2    3    4    5    6

  • Multivariate Normal Distribution

影音    1    2    3    4

  • Multiple Regression and its Applications (I)

影音    1    2    3    4

影音    1    2    3    4    5

影音    1    2    3    4    5    6    7

  • Factor Analysis and its Applications

影音    1    2    3    4    5    6    7

  • Discriminant Analysis and its Applications (I)

影音    1    2    3    4    5    6    7

  • Discriminant Analysis and its Applications (II)

影音    1    2    3    4    5    6    7

  • Cluster Analysis and its Applications (I)

影音    1    2    3    4    5

  • Cluster Analysis and its Applications (II)

影音    1    2    3    4    5    6

  • Canonical Correlation and its Applications (I)

影音    1    2    3    4    5

  • Canonical Correlation and its Applications (II)

  • Multiple Regression and its Applications (II)

  • Principal Component Analysis and its Applications

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