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Course Criteria
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3.00 Credits
The course begins with an introduction to discrete probabilities and related applications. In particular, the application of probability to sampling is studied in detail. The remainder of the course is devoted to the theory of sampling and sampling techniques. Applications are highlighted through examples and illustrated problems. Prerequisite: STT 2810 or permission of the instructor. (WRITING)
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4.00 Credits
This course provides an overview of modern statistical data analysis. Programming with data, including simulations and bootstrapping, will be an integral part of the course. Techniques for parsing univariate and multivariate data sets will be examined. Coverage of probability, random variables, standard probability distributions and statistical sampling distributions will be sufficient to prepare the student for statistical inference. Inferential topics will include parameter estimation, hypothesis testing for proportions, means and medians, goodness of fit tests, and tests for independence. Standard and computationally intensive regression techniques will also be covered. Prerequisite: MAT 1110. (NUMERICAL DATA; COMPUTER) (ND Prerequisite: passing the math placement test or successful completion of MAT 0010.)
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3.00 Credits
The goal of this course is to provide students with exposure to a variety of statistical procedures in order to develop their ability to understand statistically based research. As the course will focus on proper data analysis, sufficient practice with solving real problems using real data will be required. A variety of standard statistical methodologies will be covered including multiple regression, the analysis of variance, and the analysis of covariance. Additionally, several computationally intensive methods will be explored including, but not limited to, areas such as robust regression, bootstrapping, and permutation tests. Students will be required to complete several data analysis projects that utilize professional editing tools and demonstrate reproducible statistical research. Prerequisites: STT 3850 and ENG 2001 or its equivalent. (WRITING)
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3.00 Credits
An introduction to probability modeling. Topics include a study of sample spaces, counting rules, conditional probability and independence, random variables and their properties, and applications. Prerequisite: MAT 1120.
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3.00 Credits
This course introduces students at the post-calculus level to statistical concepts, applications, and theory. Topics include: comparisons with categorical and numerical data, statistical significance, sampling and sampling distributions, and randomized experiments. Statistical concepts will be developed through simulations, and applications will focus on statistical problem-solving. The course will introduce prospective teachers to the content and pedagogy recommended by the National Council of Teachers of Mathematics’ Standards and the American Statistical Association’s Guidelines with regard to statistics and probability at the introductory level. Prerequisite: MAT 1120. (NUMERICAL DATA; COMPUTER) (ND Prerequisite: passing the math placement test or successful completion of MAT 0010.) [Dual-listed with STT 5811.] Dual-listed courses require senior standing; juniors may enroll with permission of the department.
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3.00 Credits
This course is a continuation of STT 4811. Topics include: exploring and modeling relationships, comparing several populations, combinatorial analysis, axiomatic probability, and conditional probability. Statistical concepts will be developed through simulations, and applications will focus on statistical problem-solving. The mathematical foundations of probability will be developed and explored through simulations. The course will prepare prospective teachers to implement the National Council of Teachers of Mathematics’ Standards and the American Statistical Association’s Guidelines with regard to statistics and probability at an intermediate level. Prerequisite: STT 4811. (NUMERICAL DATA; COMPUTER) (ND Prerequisite: passing the math placement test or successful completion of MAT 0010.) [Dual-listed with STT 5812.] Dual-listed courses require senior standing; juniors may enroll with permission of the department.
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3.00 Credits
The course begins with a review of sampling, sampling distributions, and simple comparative experiments. Single factor experiments with both fixed and random effects are considered. Designs illustrated include randomized blocks, latin squares and factorial experiments. Mixed models and rules for expected mean square are presented. Model adequacy, sample size considerations, power determinations and restrictions on randomization procedures are discussed. The use of statistical software packages is integrated throughout the course. Prerequisite: STT 3820, or permission of the instructor. (WRITING) [Dual-listed with STT 5820.] Dual-listed courses require senior standing; juniors may enroll with permission of the department.
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3.00 Credits
An introduction to least squares estimation in simple and multiple regression models. The matrix approach is used in the more general multiple regression model. Considerable attention is given to the analysis of variance, aptness of the model tests, residual analysis, the effects of multicollinearity, and variable selection procedures. Prerequisites: MAT 2240 and STT 3830. (WRITING; NUMERICAL DATA; COMPUTER) (ND Prerequisite: passing the math placement test or successful completion of MAT 0010.) [Dual-listed with STT 5830.] Dual-listed courses require senior standing; juniors may enroll with permission of the department.
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3.00 Credits
Introduction to time series regression and forecasting methodologies applied to problems in business and the social sciences. Topics include forecasting with ARMA and other models, smoothing techniques, dealing with non-stationary data, time series regression, unit root tests, lags, ARCH and GARCH. Emphasis is placed upon the application of forecasting and time series regression to economic and business data using computer technology. Prerequisite: STT 3851 or permission of the instructor. (NUMERICAL DATA; COMPUTER) (ND Prerequisite: passing the math placement test or successful completion of MAT 0010.)
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3.00 Credits
A development of the mathematical foundations of probability and statistical inference. Topics include data collection and organization, counting techniques, axiomatic probability, discrete probability distributions, continuous probability distributions, sampling distributions, point and interval estimation, and tests of hypotheses on a single parameter. Prerequisite: MAT 2130.
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