本次加拿大代写主要为统计相关的assignment

STA302H1F/1001HF Methods of Data Analysis I

课程大纲
课程描述:该课程对数据分析进行了扎实的介绍,重点是
线性回归的理论与应用。涵盖的主题包括:
数据、相关性、使用最小二乘法的简单和多元回归模型、回归推理
正态分布误差参数、置信度和预测区间、模型诊断
违反模型假设时的补救措施、相互作用和虚拟变量
能力、方差分析、模型选择、惩罚回归、广义加性模型 (GAM) 和
主成分分析 (PCA)。统计软件将用于说明目的和
将需要在整个学期完成各种评估。
学习成果:在本课程结束时,所有学生都应该有一个扎实的理解
线性回归分析的数学理论及其应用
数据分析。学生应准备好通过以下方式表明他们对上述内容的理解
• 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
使用这些方法的理由;
• 用清晰的非技术语言解释数据分析结果
先决条件:先决条件由部门严格执行,而不是教师。
如果您没有同等的先决条件,您将被取消注册课程。学生们
应该有第二年的统计课程,例如 fSTA238、STA248、STA255 或 STA261g,
计算机科学,如 fCSC108、CSC120、CSC121 或 CSC148g 和数学课程,如 fMAT221(70%)、MAT223 或 MAT240g 或由部门确定的同等准备
精神。
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.


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