Indiana University Bloomington

SPEA V506
Statistical Analysis For Effective Decision-making (same As Spea E538: Environmental Statistics)

Contact: Barry Rubin (rubin@indiana.edu) 11-2-12
Offered: Fall, Every Year
Capacity: 45
Sequence: SPEA V507: Data Analysis and Modeling, which is optional for students in the SPEA MPA Program
Pre-Requisites: None
Algebra Required: Not used
Calculus Required: Not used
Contact Person for Authorization: Contact SPEA Graduate Program Office for permission.
Instructor: Barry Rubin, Haeil Jung, Ashlyn Nelson, Henry Wakunga, David Good, Vicky Meretsky
Days Per Week Offered: Two lectures per week, one computer lab per week
Recommended follow-up classes: SPEA V507 or other class on regression modeling
Syllabus: Download Syllabus
Keywords: introductory descriptive and inferential statistics, hypothesis testing, ANOVA, regression analysis
Description: Application of statistical analysis to issues in public and environmental affairs and related fields. Addresses descriptive statistics, statistical inference, the nature of random variables, sampling distributions, point and interval estimation of parameters (mean, standard deviation, etc.), hypothesis testing, analysis of variance, and bivariate and multivariate regression. Emphasizes practical aspects of applying such methods, appropriately interpreting the results of these statistical analysis tools, and gaining a meaningful understanding of how statistical analysis can be misused or erroneously executed. Use of computer tools for carrying out statistical analysis (primarily SAS) will is also a major emphasis
Books: lecture notes; Lind, Marchal, and Wathan, Statistical Techniques in Business and Economics, 15th ed., 2011
Substantive Orientation: SPEA, Social Sciences, Education, Business, Telecommunications, Anthropology, Biological and Health Sciences (E538), Informatics
Statistical Orientation: Non-experimental
Applied/Theoretical: Applied with explanations (not derivations) of statistical foundations
Software: SAS
How Software is Used: Data analysis, some programming
Problem Sets: Yes, involving analysis and interpretation of data
Data Analysis: Yes, as part of problem sets and in-class exercises
Presentation: No
Exams: Yes, midterm and final
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