STA302H1F/1001HF Methods of Data Analysis I

• 通过解决问题的问题应用方法；
• 与数学理论相关的概念的描述和解释；
• 基于线性回归概念和理论的主题推导和证明；
• 使用统计软件对真实数据的方法进行实际应用，并具有适当的

• 用清晰的非技术语言解释数据分析结果

Kèchéng dàgāng

COURSE OVERVIEW
Course Description: The course provides a solid introduction to data analysis with a focus on
the theory and application of linear regression. Topics to be covered include: initial examination of
data, correlation, simple and multiple regression models using least squares, inference for regression
parameters for normally distributed errors, con dence and prediction intervals, model diagnostics
and remedial measures when the model assumptions are violated, interactions and dummy vari-
ables, ANOVA, model selection, penalized regression, Generalized Additive Models (GAM) and
principal component analysis (PCA). Statistical software will be used for illustration purposes and
will be required for the completion of various assessments throughout the term.
Learning Outcomes: By the end of this course, all students should have a solid understanding
of both the mathematical theory of linear regression analysis and its application in the form of a
data analysis. Students should be prepared to show their understanding of the above through
• application of methods through problem-solving questions;
• description and explanation of concepts relating to the mathematical theory;
• derivation and proof of topics based on linear regression concepts and theory;
• practical application of methods on real data using statistical software, with appropriate
justi cation of use of these methods;
• interpretation of data analysis results in clear and non-technical language
Pre-requisites: Pre-requisites are strictly enforced by the department, not the instructor.
If you do not have the equivalent pre-requisites, you will be un-enrolled from the course. Students
should have a second year statistics course, such as fSTA238, STA248, STA255, or STA261g, a
computer science such as fCSC108, CSC120, CSC121, or CSC148g and a mathematics course suchas fMAT221(70%), MAT223, or MAT240g or equivalent preparation as determined by the depart-
ment.