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MATH 410 Scientific Data Analysis I 3.0 Credits

Fundamental principles and applications of statistics for scientific data analysis. Topics include data exploration, principles of probability distributions, Central Limit Theorem, hypothesis testing, z, t and F tests, one-way analysis of variance, linear regression, and contingency table analysis. Programming statistical applications in R will be included.

College/Department: College of Arts and Sciences
Repeat Status: Not repeatable for credit
Prerequisites: MATH 122 [Min Grade: D] or MATH 239 [Min Grade: D]

Elementary Education: Middle Level Science and Math

Major: Elementary Education Degree Awarded: Bachelor of Science (BS) Calendar Type: Quarter Total Credit Hours: 180.0 Co-op Options: One Co-op (Four years) Classification of Instructional (CIP) code: 13.1202 Standard Occupational Classification (SOC) code: 25-2022

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