Schedule and Textbooks Information


Fall 2017 Schedule and Textbook Information for STAT 350

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STAT 350 - Texbook(s) for Fall 2017

CRN Title Author ISBN Version Req/Opt
12022Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
13234Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
15194Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
17132Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
17748Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
20352Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
29220Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
29221Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
37879Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y
39409Introductory Statistics: A Problem Solving ApproachStephen Kokoska97814641116932Y


STAT 350 - Schedule information for Fall 2017

CRN Section Instructor Day Time Room
17132050Siddhartha NandyMF1:30-2:20pmUNIV 017
39409030Shih-Kang ChaoMF11:30am-12:20pmUNIV 017
29221020Siddhartha NandyMF3:30-4:20pmUNIV 101
20352926Nathanael LichtiMF2:30-3:20pmUNIV 019
15194060Shih-Kang ChaoMF2:30-3:20pmUNIV 101
17748901Leonore FindsenF08:30-09:20amUNIV 003
13234921Leonore FindsenF09:30-10:20amUNIV 003
37879010Siddhartha NandyMF12:30-1:20pmUNIV 017
29220040Shih-Kang ChaoMF1:30-2:20pmUNIV 101
13245903Leonore FindsenM08:30-09:20amUNIV 003
13249923Leonore FindsenM09:30-10:20amUNIV 003
20353927Nathanael LichtiW2:30-3:20pmSC 231
29224021Siddhartha NandyW3:30-4:20pmSC 231
15195061Shih-Kang ChaoW2:30-3:20pmHAMP 3144
13236902Leonore FindsenW08:30-09:20amSC 231
17133051Siddhartha NandyW1:30-2:20pmSC 231
39410031Shih-Kang ChaoW11:30am-12:20pmSC 231
29223041Shih-Kang ChaoW1:30-2:20pmHAMP 3144
13239922Leonore FindsenW09:30-10:20amSC 231
37880011Siddhartha NandyW12:30-1:20pmSC 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).