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Course Criteria
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3.00 Credits
A course for surgical assistant students to illustrate regional anatomy relevant to selected surgical procedures. Lecture presentations and cadaver dissection for instruction , demonstration and practice of competency skills.
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3.00 Credits
An introduction to statistics for students without a calculus background. The course covers data analysis (numerical summaries and graphics for describing and displaying the distributions of numerical and categorical data), the basic principles of data collection from samples and experiments, elementary probability, the application of the normal distribution to the study of random samples, statistical estimation (construction and interpretation of one sample confidence intervals), and an introduction to hypothesis testing (the structure of one sample hypothesis tests and the logic of using them to make decisions). Pre-requisite: At least 420 on the MPT strongly recommended.
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3.00 Credits
An introduction to inferential statistics for students without a calculus background. The course covers one and two-sample hypothesis tests for means and proportions, chi-squared tests, linear regression, analysis of variance, and non-parametric tests based on ranks, with attention to selecting the procedure(s) appropriate for the question and data structure, and interpreting the results. Prerequisite: Elementary Statistics I
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3.00 Credits
An introduction to probability and statistics for students with a calculus background. The course covers sample spaces and probability laws; discrete and continuous random variables with special emphasis on the binomial, Poisson, hypergeometric, normal and gamma distributions; joint distributions; sampling distributions; one and two-sample parameter estimation problems; and one and two-sample tests of hypotheses. This course provides a foundation for the further study of statistics.
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3.00 Credits
A second course in probability and statistics for students with a calculus background. This course covers chi-square tests used in goodness-of-fit problems as well as contingency tables, model building, simple and multiple linear regression, analysis of variance, experimental design, reliability, and quality control. The SAS software package may be used. This course provides a foundation for the further study of statistics.
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3.00 Credits
This occasionally offered course will allow the student to be exposed to topics in the study of statistics that are not offered as part of our regular sequence of statistics courses. It will allow students to gain appreciation for the breadth of fields that are part of modern statistical science.
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3.00 Credits
The purpose of these courses is to understand the theory of statistical inference using techniques, definitions, and concepts that are statistical and that are natural extensions and consequences of the statistical concepts. Specific topics include in Probability and Distributions, Multivariate Distributions, Some Special Statistical Distributions, Unbiasedness, Consistency, and Limiting Distributions and Central Limit Theorem.
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3.00 Credits
The purpose of these courses is to understand the theory of statistical inference using techniques, definitions, and concepts that are statistical and that are natural extensions and consequences of the statistical concepts. Specific topics include in Basics of statistical Inferences including point and interval estimation, Method of Moments and Maximum Likelihood estimation, Hypothesis testing, Sufficiency, Exponential family, Rao-Blackwell Theorem and Rao-Cramer Lower Bounds, Likelihood Ratio Tests, Neymann-Pearson Lemma and its applications.
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3.00 Credits
The purpose of these courses is to understand statistical inference and data analysis in simple linear regression model and multiple linear regression models including model selections. Specific topics include: correlation coefficient, statistical inference of parameters, checking model assumptions, variable selection, transformations of variables and diagnostics.
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3.00 Credits
The course covers the theory and application of analysis of variance with one-, two-, and higher-way layouts, random effects and mixed models. Mathematical and interpretational aspects of the models will be covered along with statistical estimation, confidence intervals and multiple hypothesis testing. SAS statistical software will be used. Specific topics include: ANOVA for some standard experimental designs.
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