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Course Criteria
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3.00 Credits
Hours and credits to be arranged
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3.00 Credits
Credits flexible; usually 4 credits per semester
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4.00 Credits
4 hours; 3 credits An introduction to statistical concepts and methods of organizing, presenting, and analyzing quantitative data. Emphasis will be on the application of these tools in making inferences and decisions from experimental and observa - tional data. Includes measurement scales; descriptive statistics; basic probability and probability distributions; concepts of sample, population, and sampling distribution; elements of statistical inference; one-way and two-way analysis of variance; and an introduction to correlation and regression analysis. The following distributions are examined and applied to the solution of problems: binomial, normal, t, and F distributions. Techniques for using the computer as a tool in the analysis of statistical problems will be introduced. (This course is appropriate for an industrial/organizational psychology or social science major. Credit can be received for only one of the following: STA 2000 or 2100.) Prerequisite: MTH 2301 or 2001 or equivalent.
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3.00 Credits
3 hours; 3 credits This course emphasizes an object-oriented approach to solving computer programming problems. Using these techniques leads to shorter system development life cycles, increased programmer productivity, code reusability, and reduced system maintenance costs. This course provides a thorough, practical knowledge of object-oriented programming methods. Students learn the principles underlying programming using a language such as C++. (This is the first part of a two-semester sequence. No prior knowledge of computer programming is required.) Prerequisite: CIS 2200.
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3.00 Credits
3 hours; 3 credits This course covers proper graphical presentations, probability models and decision making, and simple linear and multiple regression. A spreadsheet package, such as Microsoft Excel, will be used throughout the course. Not open to students who have completed STA 3154 or ECO 4000. Prerequisite: STA 2000 or equivalent.
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3.00 Credits
3 hours; 3 credits A continuation of Business Statistics I with a deeper development of topics in confidence intervals, hypothesis testing, and regression. The use of statistical packages, such as SAS or SPSS, will be integrated throughout the course. Topics covered include probability distributions, interpretation of confidence intervals and hypothesis testing results, testing in paired samples, one- and two-way analysis of variance, assumptions and analysis of regression models, and basics of nonparametric statistics. Prerequisite: STA 2000 or equivalent.
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3.00 Credits
3 hours; 3 credits This is a multiple regression and forecasting course, with applications to business, using modern statistical packages such as SAS. Among the topics covered are multiple regres - sion models, including curvilinear regression, dummy variables, and logistic regression; and time series models, including the classical multiplicative model, moving averages, exponential smoothing, and the autoregressive model. Prerequisite: STA 2000 or ECO 4000. (Not open to economics and finance majors.)
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3.00 Credits
3 hours; 3 credits Designed primarily for those who plan to employ sampling procedures in the solution of marketing, business, and industrial problems. Basic sampling theory is developed in order to ensure a mature understanding of sampling methods. The mechanics of sampling are stressed, involving such important problems as selection of sampling unit, determination of sample size, random and stratified sampling, purposive selection, sub-sampling and sampling clusters, sampling from a finite universe, the analysis of variance in the design of sample experiments, sampling limitations as a result of fixed administrative cost conditions, and area and quota sampling. Emphasis is placed on the application of sampling techniques to market research, audience analysis, and industrial quality control. Prerequisite: STA 3154.
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3.00 Credits
3 hours; 3 credits This course provides an introduction to current concepts and practices in the design and development of business applications programs. Included among the topics to be covered are the structure and features of third-generation programming languages and their use in the development of business-oriented computer software, structured programming conventions, techniques for developing solutions to business programming problems, the representation and formatting of computer data, and efficient coding tech - niques. More advanced topics such as control break, table, and sequential file update processing will also be covered. Students will be introduced to the syntax and semantics of the COBOL programming language, which will be used as the vehicle for learning. Prerequisite: CIS 2200.
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3.00 Credits
3 hours; 3 credits The application of categorical and data methods to business research. The course covers measurement scales; contingency tables (two-way and multiway), including the log-linear model; logistic regression; and categorical time series analysis. Each student will do a project involving the application of several multi-attribute methods to market research. Multi-attribute computer packages will be used to analyze the results of these projects, and oral presentations will be made to the class. Prerequisite: STA 3154.
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