社会科学统计II:多元技术
历史、政治 & 国际研究课程
马德里,西班牙
日期: 8/26/24 - 12/21/24
社会科学统计II:多元技术
概述
CEA CAPA合作机构: 马德里卡洛斯三世大学
地点: 马德里,西班牙
主要科目范围: 数学
其他学科领域: 国际关系
指令: 英语
课程代码: 16623
记录来源: 合作伙伴机构
课程详细信息: 200级
推荐学分: 3
联系时间: 42
先决条件: 社会科学统计学I或类似的统计学入门课程.
描述
话题1. 线性回归.
1.1. 线性回归. Introduction; simple and multiple regression; motivation; graphical data analysis; model formulation; dummy variables; parameter interpretation; examples; applications.
1.2. Fitting the model to the data; the least squares criterion; using the fitted model.
1.3. Model assumptions; inference on model parameters I: confidence intervals; inference on the response.
1.4. Inference on model parameters II: hypothesis testing; statistical significance of estimated parameters.
1.5. Assessing model fit; ANOVA.
1.6. Selection of predictor variables; multicollinearity; model diagnostics; model validation.
话题2. 二项逻辑回归.
2.1. Motivation; model assumptions and formulation; parameter interpretation; examples; applications.
2.2. Fitting the model to the data; using the fitted model; inference on model parameters; statistical significance of estimated parameters.
2.3. Assessing model fit; selection of predictor variables; multicollinearity.
话题3. 主成分分析.
3.1. Motivation; formulation; variance explained; examples; applications.
3.2. Deciding how many components to keep; component scores; interpretation of components; graphical representations.
主题4. 聚类分析.
4.1. Motivation; k-means clustering.
4.2. Hierarchical methods; similarity measures; dendrograms.
4.3. 应用和示例.
1.1. 线性回归. Introduction; simple and multiple regression; motivation; graphical data analysis; model formulation; dummy variables; parameter interpretation; examples; applications.
1.2. Fitting the model to the data; the least squares criterion; using the fitted model.
1.3. Model assumptions; inference on model parameters I: confidence intervals; inference on the response.
1.4. Inference on model parameters II: hypothesis testing; statistical significance of estimated parameters.
1.5. Assessing model fit; ANOVA.
1.6. Selection of predictor variables; multicollinearity; model diagnostics; model validation.
话题2. 二项逻辑回归.
2.1. Motivation; model assumptions and formulation; parameter interpretation; examples; applications.
2.2. Fitting the model to the data; using the fitted model; inference on model parameters; statistical significance of estimated parameters.
2.3. Assessing model fit; selection of predictor variables; multicollinearity.
话题3. 主成分分析.
3.1. Motivation; formulation; variance explained; examples; applications.
3.2. Deciding how many components to keep; component scores; interpretation of components; graphical representations.
主题4. 聚类分析.
4.1. Motivation; k-means clustering.
4.2. Hierarchical methods; similarity measures; dendrograms.
4.3. 应用和示例.