Schedule and Textbooks Information

Spring 2022 Schedule and Textbook Information for STAT 350

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STAT 350 - Textbook(s) for Spring 2022

CRN	Title	Author	ISBN	Version	Req/Opt
12211	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	N
13537	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	N
14964	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	N
14966	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	N
27846	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	N
42907	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	N
ALL	Introduction to Statistics Course Pack	Leonore Findsen	9781774127780		Y

STAT 350 - Schedule information for Spring 2022

CRN	Section	Instructor	Day	Time	Room
13537	IMP	Leonore Findsen	MWF	09:30-10:20am	WALC 2127
14964	500	Leonore Findsen	MWF	12:30-1:20pm	ARMS B071
42907	100	Siddhartha Nandy	MWF	2:30-3:20pm	ARMS B071
14966	400	Siddhartha Nandy	MWF	1:30-2:20pm	ARMS B071
27846	200	Siddhartha Nandy	MWF	11:30am-12:20pm	ARMS 1109
14964	500	Halin Shin	MWF	12:30-1:20pm	ARMS B071

STAT 350 - Course Outline

A. Data Analysis
Describing distributions (graphics, center and spread, comparing and selecting descriptions); Describing relationships (graphics, regression and correlation, influence, interpreting relationships).

B. Data Production
Sampling design; Design of experiments.

C. Probability distributions and simulation
The idea of a sampling distribution; The idea of probability and probability distributions; Simulating discrete and continuous distributions; Sampling distribution of sample means (law of large numbers, central limit theorem).

D. The reasoning of inference
Confidence intervals; Significance tests

E. Basic inference procedures
Inference about distributions (t procedures, robustness); Inference about proportions (z and X² procedures)

F. Regression inference
Simple linear regression (inference about slope and prediction); Introducing multiple regression (regression models, meaning of regression coefficients, interactions among explanatory variables).