1.t-test

 

 

 

Research Institutes
RENMIN UNIVERSITY OF CHINA - School of statistics

PEKING UNIVERSITY - Statistics center

NANKAI UNIVERSITY - School of statistics and data science

NORTHEAST NORMALUNIVERSITY - School of mathematics and statistics

EAST CHINA NORMAL UNIVERSITY - Academy of statistics and interdisciplinary sciences

XIAMEN UNIVERSITY - Department of Statistics

BEIJING NORMAL UNIVERSITY - School of statistics

TSINGHUA UNIVERSITY - Department of mathematics

ZHEJIANG UNIVERSITY - School of mathematical sciences

HKUST - Department of mathematics

Stanford University (Dept. of Statistics)

University of California—Berkeley (Dept. of Statistics)

Harvard University (Dept. of Statistics)

University of Chicago (Dept. of Statistics)

Carnegie Mellon University (Dept. of Statistics)

Columbia University (Dept. of Statistics)

University of Wisconsin—Madison (Dept. of Statistics)

University of Cambridge (Dept. of Pure Mathematics and Mathematical Statistics)

University of Oxford (Dept. of Statistics)

University of Toronto (Dept. of Statistics)

MIT (Statistics)

Imperial College London (The Statistics section within the Mathematics Department)

National University of Singapore (Dept. of Statistics)

 

t检验最常见的四个用途

单样本均值检验（One-sample t-test）
用于检验 总体方差未知、正态数据或近似正态的 单样本的均值 是否与 已知的总体均值相等
两独立样本均值检验（Independent two-sample t-test）
用于检验 两对独立的 正态数据或近似正态的 样本的均值 是否相等，这里可根据总体方差是否相等分类讨论
配对样本均值检验（Dependent t-test for paired samples）
用于检验 一对配对样本的均值的差 是否等于某一个值
回归系数的显著性检验（t-test for regression coefficient significance）
用于检验 回归模型的解释变量对被解释变量是否有显著影响

结构方程模型(Structural Equation Model, SEM)功能

可以分析有多个因变量的模型(multiple dependent variables)
可以分析复杂的中介模型(complex mediating mechanisms)
可以估算潜在变量以解释测量误差(estimate latent variables)
可以估算二分变量(binary) /序级变量(ordinal)的潜在因子
可以测试跨组的模型不变性（test invariance of models across groups)
可以建模重复测量的数据的发展轨迹(growth trajectories of repeated measures)

Statistics is a wonderful discipline. It has it all: mathematics and philosophy, analysis and empiricism, as well as applicability, relevance, and the fascination of data. It demands clear thinking, good judgment, and flair. Statisticians are engaged in an exhausting but exhilarating struggle with the biggest challenge that philosophy makes to science: how do we translate information into knowledge? Statistics tells us how to evaluate evidence, how to design experiments, how to turn data into decisions, how much credence should be given to whom to what and why, how to reckon chances, and when to take them. Statistics deals with the very essence of the universe: chance and contingency are its discourse and statisticians know the vocabulary. If you think that statistics has nothing to say about what you do or how you could do it better, then you are either wrong or in need of a more interesting job. ~ Stephen Senn. Dicing with Death: Chance, Risk and Health