◆我们用多元统计: 1、用各科成绩的总和作为综合指标,来比较 学生学习的好坏。 2、根据各科成绩相近程度对学生进行分类 (成绩好的与差的;文科成绩好的和理科成 绩好的等等) 3、各科成绩之间的关系(如物理与数学成绩 的关系;文科成绩与理科成绩的关系等) 2021/2/22 cxt
2021/2/22 11 cxt ❖ 我们用多元统计： 1、用各科成绩的总和作为综合指标，来比较 学生学习的好坏。 2、根据各科成绩相近程度对学生进行分类 （成绩好的与差的；文科成绩好的和理科成 绩好的等等） 3、各科成绩之间的关系（如物理与数学成绩 的关系；文科成绩与理科成绩的关系等）
◆多元统计分析优点:分析问题更全面更透彻 ◆能使我们对所研究的问题更全面,更深刻的认识帮 助我们透过现象看本质发观事物之间内在的本质 规律。 ◇多元统计分析是研究多个随机变量之间相互 依赖关系以及內在统计规律的一门统计学科 2021/2/22 cxt
2021/2/22 12 cxt ❖ 多元统计分析优点：分析问题更全面更透彻 ❖ 能使我们对所研究的问题更全面, 更深刻的认识.帮 助我们透过现象看本质,发观事物之间内在的本质 规律。 ❖ 多元统计分析是研究多个随机变量之间相互 依赖关系以及内在统计规律的一门统计学科
2 Types of Multivariate Techniques Dependence Techniques- variables are divided into dependent and independent. Dependence techniques attempt to explain or predict the dependent variable(s)on the basis of two or more independent variables include Multiple Regression(MR), Discriminant Analysis (DA), Multivariate Analysis of Variance (MANOVA), canonical Correlation AnalysiS(CCA) Interdependence Techniques-all variable s are analyze simultaneously, with none being designated as either dependent or independent. The goal of interdependence techniques is to give meaning to a set of variables or seek to group things together. no one variable or variable subset is to be predicted from the others or explained by them include Principal Components Analysis(PCa), Factor Analysis(FA), Cluster analysis(ca multidimensional scaling(mish 2021/2/22 13 cxt
2021/2/22 13 cxt 2、Types of Multivariate Techniques ❖ Dependence Techniques – variables are divided into dependent and independent. Dependence techniques attempt to explain or predict the dependent variable(s) on the basis of two or more independent variables.include Multiple Regression (MR), Discriminant Analysis (DA) , Multivariate Analysis of Variance (MANOVA) , Canonical Correlation Analysis (CCA). ❖ Interdependence Techniques – all variables are analyzed simultaneously, with none being designated as either dependent or independent. The goal of interdependence techniques is to give meaning to a set of variables or seek to group things together. No one variable, or variable subset is to be predicted from the others or explained by them. include Principal Components Analysis (PCA), Factor Analysis (FA), Cluster Analysis (CA), Multidimensional Scaling (MDS)
Dependence Techniques 1. Multiple regression(MR) 2. Discriminant Analysis (DA) 3. Multivariate Analysis of Variance MANOVA 4. Canonical Correlation AnalysiS(CCA) 2021/2/22 cxt
2021/2/22 14 cxt ❖ Dependence Techniques 1. Multiple Regression (MR) 2. Discriminant Analysis (DA) 3. Multivariate Analysis of Variance (MANOVA) 4. Canonical Correlation Analysis (CCA)
. Interdependence techniques 1. Principal Components Analysis(PCa) 2. Factor Analysis(FA) 3. Cluster analysis(CA) 4. Multidimensional Scaling(MDS) 2021/2/22 15 cxt
2021/2/22 15 cxt ❖ Interdependence techniques 1. Principal Components Analysis (PCA) 2. Factor Analysis (FA) 3. Cluster Analysis (CA) 4. Multidimensional Scaling (MDS)