Schedule and Textbooks Information

Spring 2021 Schedule and Textbook Information for STAT 350

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STAT 350 - Textbook(s) for Spring 2021

CRN	Title	Author	ISBN	Version	Req/Opt
12211	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
13537	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
14964	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
14966	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
26817	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
27846	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
33981	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
42907	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y

STAT 350 - Schedule information for Spring 2021

CRN	Section	Instructor	Day	Time	Room
12211	999	Leonore Findsen		0:0-0:0am	ASYNC ONLINE
26817	OL1	Piyas Chakraborty		0:0-0:0am	ASYNC ONLINE
12211	999	Hao Xin		0:0-0:0am	ASYNC ONLINE
13537	IMP	Leonore Findsen	MWF	09:30-10:20am	WALC 2007
27846	200	Piyas Chakraborty	MWF	1:30-2:20pm	HAMP 1144
42907	100	Piyas Chakraborty	MWF	12:30-1:20pm	HAMP 1144
14964	500	Siddhartha Nandy	MWF	10:30-11:20am	STEW 320
14966	400	Siddhartha Nandy	MWF	09:30-10:20am	STEW 320
33981	300	Siddhartha Nandy	MWF	11:30am-12:20pm	STEW 320

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).