Schedule and Textbooks Information


Spring 2018 Schedule and Textbook Information for STAT 350

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STAT 350 - Texbook(s) for Spring 2018

CRN Title Author ISBN Version Req/Opt
12211Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
14964Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
14966Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
18131Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
27845Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
27846Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
33981Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
42907Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y


STAT 350 - Schedule information for Spring 2018

CRN Section Instructor Day Time Room
18131901Leonore FindsenF08:30-09:20amUNIV 019
33981040Shih-Kang ChaoMF12:30-1:20pmUNIV 001
14964074Siddhartha NandyMF2:30-3:20pmUNIV 101
27845020Shih-Kang ChaoMF09:30-10:20amME 1051
42907050Siddhartha NandyMF3:30-4:20pmUNIV 101
14966076Siddhartha NandyMF1:30-2:20pmUNIV 101
27846070Shih-Kang ChaoMF11:30am-12:20pmUNIV 001
18136903Leonore FindsenM08:30-09:20amUNIV 019
14969079Siddhartha NandyW2:30-3:20pmSC 231
18133902Leonore FindsenW08:30-09:20amSC 231
49870041Shih-Kang ChaoW11:30am-12:20pmSC 231
49871071Siddhartha NandyW1:30-2:20pmSC 231
49869021Shih-Kang ChaoW12:30-1:20pmSC 231
14967077Shih-Kang ChaoW09:30-10:20amSC 231
49872051Siddhartha NandyW3:30-4:20pmSC 231

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).
Last Updated: Oct 10, 2017 11:37 AM

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