課程介紹
多變數分析是以統計學為基礎，運用綜合分析的統計方法來同時測量多個變數(植物性狀)的資料，適合探討農業上多個研究對象和多個指標之關聯性分析。
教科書:
（1）Sharma S (1996) Applied multivariate techniques. John Wiley & Sons.
（2）Hair Jr JF, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis (7th edition). Pearson Prentice Hall
（3）Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, (6th edition). Upper Saddle River, NJ: Pearson Prentice Hall
（2）Hair Jr JF, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis (7th edition). Pearson Prentice Hall
（3）Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, (6th edition). Upper Saddle River, NJ: Pearson Prentice Hall
教學進度:
（1）Introduction: types of measurement scales; classification of multivariate analysis; dependence/interdependence methods.
（2）Matrix algebra & random vector: basic matrix/vector algebra; linear combination of random variables.
（3）Data manipulation: missing data, outliers, assumptions, data transformation.
（4）Principal components analysis: introduction; analytical approach; PCA; examples; issues.
（5）Factor analysis: basic concepts; objective of factor analysis; orthogonal factor model; methods of estimation; examples.
（6）Confirmatory factor analysis: basic concepts; the methods.
（7）Cluster analysis: introduction; similarity measures; hierarchical clustering; clustering methods; examples.
（8）Two-group discriminant analysis: geometric view; analystic appraoch; discriminant analysis; regression approach; examples.
（9）Multiple-group discriminant analysis: geometric view; analystic appraoch; MDA; examples.
（10）Logistic regression: basic concepts; glm and contingency table; examples.
（11）Multivariate analysis of variance: MANOVA; two-group MANOVA; multiple-group MANOVA; examples.
（12）Assumptions: significance and power of test statistics; normality assumptions; testing univariate normality; testing for multivariate normality.
（13）Canonical correlation analysis: canonical correlation; four types of correlation analysis; examples.
（14）Path analysis: graphical models; path coefficient; causal model.
（15）Covariance structure models: structure models with (un)observable constructs; examples.
（2）Matrix algebra & random vector: basic matrix/vector algebra; linear combination of random variables.
（3）Data manipulation: missing data, outliers, assumptions, data transformation.
（4）Principal components analysis: introduction; analytical approach; PCA; examples; issues.
（5）Factor analysis: basic concepts; objective of factor analysis; orthogonal factor model; methods of estimation; examples.
（6）Confirmatory factor analysis: basic concepts; the methods.
（7）Cluster analysis: introduction; similarity measures; hierarchical clustering; clustering methods; examples.
（8）Two-group discriminant analysis: geometric view; analystic appraoch; discriminant analysis; regression approach; examples.
（9）Multiple-group discriminant analysis: geometric view; analystic appraoch; MDA; examples.
（10）Logistic regression: basic concepts; glm and contingency table; examples.
（11）Multivariate analysis of variance: MANOVA; two-group MANOVA; multiple-group MANOVA; examples.
（12）Assumptions: significance and power of test statistics; normality assumptions; testing univariate normality; testing for multivariate normality.
（13）Canonical correlation analysis: canonical correlation; four types of correlation analysis; examples.
（14）Path analysis: graphical models; path coefficient; causal model.
（15）Covariance structure models: structure models with (un)observable constructs; examples.
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