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Course Criteria
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3.00 Credits
Pr. BCN 171 or departmental permission May be repeated for credit when the topic varies. Works of an individual film director. Subject differs from offering to offering. (Same as FRE 561, ITA 517)
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3.00 Credits
Pr. BCN 171 or departmental permission May be repeated for credit when the topic varies. Technical, dramatic, social, and rhetorical dimensions of a film genre or genres. Subject differs from offering to offering. (Same as FRE 562, ITA 518)
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3.00 Credits
Pr. grade of at least C in MAT 119 or 150 or STA 108 or permission of department Survey of basic descriptive and inferential statistics. Graphs and descriptive measures, simple linear regression and correlation, data collection, basic probability and probability models, interval estimation and significance testing, analysis of variance, use of statistical software. An appropriate preparation for more advanced statistics courses in any discipline. (Fall & Spring)
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3.00 Credits
Pr. MAT 292 or permission of instructor Introduction to probability models and statistical inference. Descriptive statistics, basic probability laws, discrete and continuous probability models, sampling distributions, central limit theorem, estimation, hypothesis testing, simple regression, and correlation. (Fall or Spring)
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3.00 Credits
Pr. 271 or 290 or permission of instructor Two-group comparisons, simple and multiple regression, one and two factor ANOVA, categorical data analysis, nonparametric methods. (Spring)
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3.00 Credits
Pr. grade of at least C in MAT 292 Basic probability theory; combinatorial probability, conditional probability and independent events; univariate and multivariate probability distribution functions and their properties. (Fall)
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3.00 Credits
Pr. grade of at least C in STA 290 or permission of instructor Descriptive and inferential statistics. Emphasis on sampling distributions; theory of estimation and tests of hypotheses, linear hypothesis theory, regression, correlation and analysis of variance. (Spring)
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3.00 Credits
Pr. grade of at least C in STA 291 Introduction to statistical methods for data mining; classification and prediction methods using regression and discrimination techniques; clustering methods using distance, linkage, hierarchical methods. Using statistical software to perform data mining.
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3.00 Credits
Pr. STA 291 or permission of instructor Designing survey instruments; estimation of population mean, total, and proportion using simple random, stratified, systematic, and cluster sampling; other sampling techniques such as pps sampling and randomized response methods. (Alt)
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3.00 Credits
Pr. STA 291 or permission of instructor One and two sample permutation and rank tests, k- sample tests, tests of association, contingency table analysis, nonparametric bootstrapping. (Alt)
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