Analytics Minor (23 Credit Hours)
The analytics minor is a useful complement to majors in health or natural sciences. Like other structured minors, it offers greater employment potential.
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This course includes the underlying fundamental mathematical principles and their
application to a wide range of statistical methods and tests. Included are the following:
sampling, frequency distributions, probability, regression,confidence intervals, hypothesis
testing, t-test, analysis of variance, chi-square and correlation. Existent computer
software such as MiniTab is utilized by students to aid and facilitate the analysis
of results. Not open to those who have taken MAT 120
This course continues and expands the material present in MAT 123. The course will
cover hypothesis testing for variances, symmetric versus asymmetric distributions,
non-parametric methods for one, two or multiple samples, measures of association,
multifactor analysis of variance, and analysis of covariance. The material focuses
on the application of known methods. Large data sets will be employed to explore the
methods presented in class. The course will employ one of SPSS, MINITAB or SAS.
The course covers the ideas behind, application of, and evaluation of regression processes,
which are used to explore the relationships between variables. This course will cover
simple linear regression, multiple linear regression, regression diagnostics, use
of qualitative variables as predictors, transformations of variables, collinear data,
and logistical regression. The material focuses on the application of known methods.
Large data sets will be employed to explore the methods presented in class. The course
will employ one of SPSS, MINITAB, or SAS.
Students will learn about various types of relational database programs and understand
the fundamental aspects of SQL (Structured Query Language). This course covers database
concepts, design concepts, database administration, and web-based databases. Students
will receive an introduction to the SAS programming language with a focus on manipulation,
summarizing, and basic statistical analysis of large data sets.
This course provides an introduction to common experimental designs in the health
sciences, such as clinical trials, case-control studies, and cohort studies, and the
statistical methods used in those studies, including odds ratios, relative risk, logistic
regression, longitudinal analysis, and survival analysis. Emphasis is placed on practical
data analysis in biology and medicine. The course will employ one of SPSS, MINITAB
The course will cover the process of statistical inquiry, including defining the problem,
hypotheses development, selection of appropriate variables, test selection, interpretation
of results, and reporting of conclusions. Large data sets will be employed to explore
the methods presented in class. Group projects and oral presentations will simulate
real life job experiences in the analytics industry. This course will employ one of
SPSS, MINITAB or SAS.