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Course Criteria
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4.00 Credits
Descriptive statistics. Probability and counting rules. Random variables and probability distributions. Sampling distributions and point estimation. Confidence intervals and tests of hypotheses for a single mean and proportion. 4 lectures. Prerequisite: Completion of the ELM requirement and a passing score on appropriate Mathematics Placement Examination for MATH 221 eligibility, or MATH 118 or equivalent.
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5.00 Credits
Confidence intervals and tests of hypotheses for two means and two proportions. Introduction to ANOVA, regression, correlation, multiple regression, time series, and forecasting. Statistical quality control. Enumerative data analysis. Substantial use of statistical software. 5 lectures. Prerequisite: STAT 251 with a minimum grade of C-.
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4.00 Credits
Introduction to statistics for mathematically inclined students, focused on process of statistical investigations. Observational studies, controlled experiments, randomization, confounding, randomization tests, hypergeo-metric distribution, descriptive statistics, sampling, bias, binomial distribution, significance tests, confidence intervals, normal model, t- procedures, two-sample procedures. Substantial use of statistical software. Not open to students with credit in STAT 322. 4 lectures. Prerequisite or concurrent: MATH 142. Changed effective Fall 2008.
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4.00 Credits
Continued study of the process, concepts, and methods of statistical investigations. Association, chi-square procedures, one-way ANOVA, multiple comparisons, two-way ANOVA interaction, simple linear regression, correlation, prediction, logistic regression, multiple regression, time series, forecasting, quality control. Substantial use of statistical software. Not open to students with credit in STAT 322. 4 lectures. Prerequisite: STAT 301.
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4.00 Credits
Descriptive and graphical methods. Discrete and continuous probability distributions. One and two sample confidence intervals and hypothesis testing. Single factor analysis of variance. Quality control. Introduction to regression and to experimental design. Substantial use of statistical software. 4 lectures. Prerequisite: MATH 142.
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4.00 Credits
Analysis of variance and regression analysis for students not majoring in statistics or mathematics. Includes one-way classification, randomized blocks, Latin squares, factorial designs, multiple regression, diagnostics, and model comparison. 4 lectures. Prerequisite: STAT 217 or STAT 218 or STAT 221.
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4.00 Credits
Tabular and graphical methods for data summary, numerical summary measures, probability concepts and properties, discrete and continuous probability distributions, expected values, statistics and their sampling distributions, point estimation, confidence intervals for a mean and proportion. Use of statistical software. 4 lectures. Prerequisite: MATH 142.
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4.00 Credits
Confidence intervals, hypothesis testing, one and two-factor analysis of variance, simple linear regression, nonlinear and multiple regression, chi-square tests, introduction to statistical quality control. Substantial use of statistical software. 4 lectures. Prerequisite: STAT 321.
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4.00 Credits
Principles, construction and analysis of experimental designs. Includes completely randomized, randomized complete block, Latin squares, Graeco Latin squares, factorial, and nested designs. Fixed and random effects, expected mean squares, multiple comparisons, and analysis of covariance. 4 lectures. Prerequisite: STAT 302 or STAT 312 or STAT 322.
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4.00 Credits
Simple linear regression and associated special topics, multiple linear regression, indicator variables, influence diagnostics, assumption analysis, selection of "best subset", nonstandard regression models, logistic regression, nonlinear regression models. 4 lectures. Prerequisite: STAT 252 or STAT 302 or STAT 312 or STAT 313 or STAT 322.
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