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STA213lecture1.notes_1 - 1 1.1 Introduction Class logistics...

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1 Introduction 1.1 Class logistics General information Instructor: Ioanna Manolopoulou, Old Chem 217, [email protected] Office hours MWF 1.30-2.30pm TA: Tommy Leininger, [email protected] Silvia Montagna, [email protected] Course website www.stat.edu/ im30/Teaching/STA213 HW: due once a week Midterm: 7th Oct Grades and materials through Blackboard (inc notes and slides) Textbook: Casella & Berger, 2nd edition Thursday 09/02: short diagnostic (ungraded) quiz 2 What is Statistics? Statistics [Utts, Seeing Through Statistics] A collection of procedures and principles for gaining and analyzing information in order to help people make decisions when faced with uncertainty. How do we gain information? Design of experiments. How do we analyze information? How does uncertainty come into it? Probability models. What do the data tell us? Statistical Inference. How do we change our behaviour according to the results? Decision theory. Obtain sample to conclude something about the population. 3 Course outline Course content Basic probability theory, Random variables, Expectations, Transformations Common univariate distribution families: Discrete and Continuous. Examples in R. Common multivariate distribution families. Examples in R. Concepts of convergence We will roughly cover Chapters 1-5 from Casella & Berger. Chapters 6-12 are covered in STA215. I will be using a combination of slides and blackboard. In general, theorems will appear in the slides, but proofs will be shown on the blackboard.
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4 Today Lecture content Notation Set theory in probability Introduction to probability theory Examples 5 Notations Experiments
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