Schedule and Textbooks Information

Fall 2020 Schedule and Textbook Information for STAT 350

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STAT 350 - Textbook(s) for Fall 2020

CRN	Title	Author	ISBN	Version	Req/Opt
12022	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
15194	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
17132	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
20352	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
25881	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
29220	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
29221	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
37879	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y
39409	Introductory Statistics: A Problem Solving Approach	Stephen Kokoska	9781319049621	3rd	Y

STAT 350 - Schedule information for Fall 2020

CRN	Section	Instructor	Day	Time	Room
17132	IMB	Leonore Findsen	MWF	09:30-10:20am	WALC B058
29221	IMA	Leonore Findsen	MWF	08:30-09:20am	WALC 2007
15194	130	Piyas Chakraborty	MWF	1:30-2:20pm	STEW 302
20352	230	Piyas Chakraborty	MWF	2:30-3:20pm	STEW 302
37879	123	Siddhartha Nandy	MWF	12:30-1:20pm	MATH 175
29220	103	Siddhartha Nandy	MWF	10:30-11:20am	MATH 175
39409	113	Siddhartha Nandy	MWF	11:30am-12:20pm	MATH 175

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).