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Data may come from various sources and studies with different purpose of analysis. Statistical modeling provides a unified framework to embrace different data types, and focuses on the goals of understanding relationships, assessing differences and making predictions. We will explore different types of statistical models (linear regression, ANOVA, logistic regression etc), and focus on their conditions, the interactive modeling process, as well as the statistical inference tools for drawing conclusions from them. Throughout the course, real datasets will be modeled for interesting questions about the world, and the limitations will be addressed as well.
Format: lecture; This will be a hybrid course for students who are both remote and in-person, with a mix of synchronous and asynchronous elements.
Grading: yes pass/fail option,
no fifth course option
weekly homework assignments (consist of both theoretical and applied questions), short quizzes, possibly two exams and a course project.
One of the following: i) STAT 201; ii) MATH 140 and STAT 101/161/AP Statistics 4/5; iii) Permission of instructor
students interested in statistics which have solid background in math and stat
It is an advanced statistics class with prerequisites that are QFR courses