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Accounting 010
African Area Studies 016
African, Middle Eastern, and South Asian Languages and Literatures 013
Africana Studies 014
Agriculture and Food Systems 020
American History 512
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French 420
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Individualized Major 555
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Italian 560
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Junior Year Abroad
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Microbiology 680
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Military Education, Air Force 690
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Military Science Minor (Military Science 691N, Naval Science 692N, Aerospace Science 693N, Non-Commissioning 695N)
Molecular Biology
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Operations Research 711
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Polish 787
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Religion 840
Russian 860
Sexualities Studies 888
Social Justice 904
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Sociology 920
South Asian Studies 925
Spanish 940
Sport Management 955
Statistics 960
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Study Abroad 959
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Camden Newark New Brunswick/Piscataway
New Brunswick Undergraduate Catalog 2022-2024 Programs of Study and Courses for Liberal Arts and Sciences Students Programs, Faculty, and Courses Statistics 960 Courses  


In the following course list, the Level II Statistics prerequisite may be fulfilled with 01:960:212 or 291 or 384 or 401 or 484 or equivalent. Credit is not given for more than one course fulfilling the Level II Statistics prerequisite.

01:960:142 Data 101 (3) Topics in data literacy for students not majoring in computer science or statistics. Prerequisite: 01:640:025 or placement. Credit not given for both this course and 01:198:142.
01:960:211,212 Statistics I,II (3,3) Principles and methods of statistics, including probability distributions, sampling, estimation, hypothesis testing, regression and correlation analysis, chi-square analysis, analysis of variance, tests of significance. Application of statistical techniques to the analysis of data, tests of significance, correlation and regression analysis, confidence intervals, analysis of variance and some design of experiments, analysis of cross-classified data, chi-square tests.
Prerequisite: 01:640:115 or permission of department. See Level II Statistics restrictions. Credit not given for both this course and either 01:960:384 or 484. Credit not given for both this course and 01:960:401 unless 401 is being used, with permission of the department, as a prerequisite for this course.
01:960:285 Introductory Statistics for Business (3) Topics include descriptive statistics, probability, random variables, sampling distributions, principles of hypothesis testing, and one- and two-sample t-tests. Prerequisite: 01:640:115 or equivalent. Credit not given for more than one of 01:960:201, 211, 285, or 401.
01:960:291 Statistical Inference for Data Science (3) Introduction to probability and statistics underlying data science. Topics include regression, resampling, confidence intervals, hypothesis testing, and related probability distributions. Prerequisites: 01:640:115 and (01:198:142 or 01:960:142).
01:960:295 Data Management and Wrangling with R (3) An introduction to the tools and principles to retrieve, tidy, clean, and visualize data in preparation for statistical analysis. The R statistical environment is used but no prior knowledge is required. Interactions with databases will be included. Prerequisite: 01:198:142 or 01:960:142 or 01:960:291 or 01:960:212 or 01:960:384 or 01:960:401.
01:960:365 Introduction to Bayesian Data Analysis (3) Principles of Bayesian data analysis and application of them to varied data analysis problems. Topics include: Bayes Theorem, linear and nonlinear models, hierarchical models, and the use of Markov chain Monte Carlo methods. Prerequisites: Level II Statistics and 01:640:152.
01:960:379 Basic Probability and Statistics (3) Methods of presenting data; basic statistical measures of location; frequency distributions; elementary probability theory; probability distributions; the binomial, Poisson, and normal distributions; basic sampling theory. Prerequisite: One semester of calculus.
01:960:381 Theory of Probability (3) Probability distributions; the binomial, geometric, exponential, Poisson, and normal distributions; moment-generating functions; sampling distributions; applications of probability theory. Prerequisites: Three semesters of calculus.
01:960:382 Theory of Statistics (3) Statistical inference methods, point and interval estimation, maximum likelihood estimators, information inequality, hypothesis testing, Neyman-Pearson lemma, linear models. Prerequisite: 01:960:381 or equivalent.
01:960:384 Intermediate Statistical Analysis (3)   Application of statistical techniques to the analysis of data, tests of significance, correlation and regression analysis, confidence intervals, analysis of variance, and some design of experiments; analysis of cross-classified data, chi-square tests.
Prerequisite: One of the following courses: 01:960:201, 211, 285, 379, 381, or permission of instructor. Credit not given for both this course and 01:960:212 or 484. Credit not given for both this course and 01:960:401 unless 401 is being used, with permission of the department, as a prerequisite for this course.
01:960:390 Introductory Computing for Statistics (1) Introduction to the use of statistics computer packages with main focus on the SAS system. Includes generating random samples, estimation, testing hypothesis, ANOVA. Five-week course; 3 hrs. lec. and lab. Prerequisite: Level II Statistics. Graded on a Pass/Fail (undergraduate) and S/U (graduate) basis.
01:960:391,392 Honors Seminars in Probability/Statistics (3,3) Real-life examples or case studies on statistics and probability theory and their ramifications. Topics may vary each semester. Extensive data analysis required. Prerequisite: Calculus 1 or permission of department. Corequisite: Calculus 2. Open to students in school honors programs.
01:960:401 Basic Statistics for Research (3) As applied in fields other than statistics; treats research projects dependent on the use of observed data from planned experiments. Includes inference methods in estimation and hypothesis testing and general linear models. Prerequisite: 01:640:115 or equivalent. Credit not given for both this course and 01:960:211, 212, 384, and/or 484. Students who have already taken 01:960:401 and wish to take any of these courses should consult with the department.
01:960:435 Seminar in Probability and Statistical Inference (3) Examines statistical theory, including topics such as Bayesian inference, confidence intervals, and maximum likelihood, to prepare students to use, discuss, and criticize statistical methods in a way that is both mathematically sound and historically informed. Prerequisite: 01:960:382 or equivalent or permission of instructor.
01:960:463 Regression Methods (3) Multiple and nonlinear correlation and regression techniques for analysis of events in time and space: analysis of variance and covariance, related multivariate techniques, response surface approaches. Prerequisite: Level II
01:960:467 Applied Multivariate Analysis (3) Introduction to the methodology of multivariate analysis. Multiple linear regression, discriminant analysis, profile analysis, canonical correlation, principal components, and factor analysis. Prerequisite: Level II-Statistics.
01:960:476 Introduction to Sampling (3) Principles of sampling applied for economical procurement or assessment of data. Current techniques for area sampling, sampling of accounts, large-scale surveys, stratification, cluster sampling, systematic sampling, two-stage sampling, and construction of estimates. Prerequisites: Level II Statistics and 01:960:379 or 381 or equivalent with a grade of C or better, or permission of department.
01:960:482 Advanced Issues in Probability and Statistics (3) Focus on understanding both the power and limitations of statistical techniques and principles as applied in real-world settings. Includes projects involving the collection of data to be analyzed and discussed. Prerequisites: 01:960:382 and permission of instructor.
01:960:483 Statistical Quality Control (3) Statistical measures; histogram analysis; construction and analysis of control charts for variables and attributes; use of Dodge-Romig and Military Standards acceptance sampling plans; statistical aspects of tolerances. Prerequisite: Level II Statistics or 33:623:385.
01:960:484 Basic Applied Statistics (3) Confidence estimation, hypothesis testing, chi-square methods, correlation and regression analysis, basis of design of experiments. Prerequisite: One of the following courses: 01:960:201, 211, 285, 379, 381, or permission of instructor. Credit not given for both this course and 01:960:212 or 384 or 401.
01:960:486 Applied Statistical Learning (3)
Introduction to machine learning methods from a statistical perspective. Both traditional methods such as logistic regression, naive Bayes classification, and discriminant analysis as well as more recent statistical methods such as regularization, boosting, and random forests will be applied to datasets of varying size. The emphasis will not be on programming the techniques from scratch but on understanding and applying the methods using a high level language such as R. Prerequisites: Level II Statistics.
Prerequisite: Level II Statistics.
01:960:490 Introduction to Experimental Design (3) Basic concept and principles of designs. Nature and analysis of various designs; randomized blocks, Latin squares, factorial designs. Applications to specific problems. Prerequisite: Level II Statistics.
01:960:491 Reliability-Quality Control (3) Survey of current theory and practice in this field. Prerequisites: 01:640:251 and Level II Statistics.
01:960:492 Special Topics in Statistics and Probability (3) Readings and presentations related to the development of probability and statistics. Prerequisite: Permission of instructor.    
01:960:495 Independent Studies in Statistics (3) Prerequisite: Permission of department.
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