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Course Criteria
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1.00 - 3.00 Credits
No course description available.
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1.00 - 12.00 Credits
No course description available.
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4.00 Credits
4 credits (4+0) GER GER. Descriptive statistics, estimations, statistical . tests. Prerequisite: MATH S105 (C or higher) or placement test.
Prerequisite:
MATH S105 US C Concurrent (OR AAEA 090 AND AACM 063 ) OR AX1 030 OR AX2 030 OR AX3 030 OR AX4 030 OR AX5 030 OR MATH A105 UA C Concurrent OR DEVM F105 UF C Concurrent OR MATH V105 UV C Concurrent
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1.00 - 6.00 Credits
No course description available.
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3.00 Credits
3 credits (3+0) GER. Introduction to concepts and applications of elementary statistical methods. Topics include sampling and data analysis, descriptive statistics, elementary probability, probability and sampling distributions, confidence intervals, hypothesis testing, correlation, and simple linear regression. Prerequisite: MATH S105 (B or better) or placement test. Recommended: MATH S151 (C or better).
Prerequisite:
MATH S105 US B Concurrent OR MATH A105 UA B Concurrent OR DEVM F105 UF B Concurrent OR AACM 063 OR AX1 055 OR AX2 055 OR AX3 055 OR AX4 055 OR AX5 055
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3.00 Credits
3 credits (3+0) A calculus-based course emphasizing theory and applications. Topics include probability, continuous and discrete random variables and their probability distributions, expectation, moment generating functions, joint distributions, functions of random variables, estimations, and an introduction to the study of the power and significance of hypothesis tests. Prerequisites: MATH S252, C (2.00) or higher.
Prerequisite:
MATH S252 US C Concurrent OR MATH S201 US C Concurrent OR MATH A252 UA C Concurrent OR MATH A201 UA C Concurrent OR MATH F252 UF C Concurrent OR MATH F201X UF C Concurrent OR MATH S202 US D- Concurrent OR MATH S253 US D- Concurrent
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1.00 - 6.00 Credits
No course description available.
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1.00 - 6.00 Credits
No course description available.
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2.00 Credits
2 credits (0+4) An in-depth introduction to the fundamentals of programming with R, the free open-sourced statistical software. Emphasizes development of skills in preparing user-defined functions and code via topics introduced in traditional elementary statistics courses. Includes descriptive statistics, graphical and numerical methods for exploring univariate and bivariate data, interval estimates, one- and two-sample hypothesis tests, one-factor ANOVA, correlation, simple regression, bivariate least squares curve fitting, contingency tables, and nonparametric methods. Prerequisite: STAT S200 (C or higher).
Prerequisite:
STAT S273 US C
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4.00 Credits
4 credits (3+3) A study of multiple regression including multiple and partial correlation, the extra sum of squares principle, indicator variables, and model selection techniques. Analysis of variance and covariance for multi-factor studies in completely random and randomized complete block designs, multiple comparisons and orthogonal contrasts. Prerequisite: STAT S200 with C (2.00) or better, or equivalent (or higher); or instructor permission. STAT S400 recommended.
Prerequisite:
STAT S273 US C Concurrent
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