| source City University of New York (X) |
level |
department Statistics (X) |
A one-semester broad-based introductory business statistics course that focuses on descriptive statistics, control charts, regression, and inferential statistics. Topics covered include graphical methods, descriptive statistics with exploratory data analysis, an introduction to control charts (with a focus on special cause and common cause variation), linear regression and correlation, the normal distribution and sampling distribution of the mean, estimation for means and proportions, and hypothesis testing for one and two groups. Students will use a microcomputer statistical package for analyzing selected data sets.
Score: 8.198331 Details | Listing | Web page
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 observational 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.
Score: 8.198331 Details | Listing | Web page
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.
Score: 8.198331 Details | Listing | Web page
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.
Score: 8.198331 Details | Listing | Web page
Multiple regression and forecasting as applied to business, using the statistical package SAS. Among the topics covered are multiple regression 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.
Score: 8.198331 Details | Listing | Web page
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.
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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.
Score: 8.198331 Details | Listing | Web page
The theory and application of statistical control techniques to industrial and nonindustrial processes. The theory of the control chart method, including sensitivity measurement and the design of process control systems. Acceptance sampling, including both variables and attributes, is surveyed. Operating characteristic curves and various criteria are also included. Statistical and mathematical concepts are explored.
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This course emphasizes the mathematical theory of probability and statistics. Students are expected to have a working knowledge of topics including axioms of probability, independence, conditional probability and Bayes' theorem, data generating processes, random variable and their distribution. Some of the probability distributions developed are binomial, Pascal exponential. Poisson, gamma, normal and multivariate. Other topics considered include t, X , F, Erlang and Weibull distributions, central limit theorem, generating functions, law of large numbers, random sampling distributions, introduction to stochastic processes and their application to the construction of statistical models. Those aspects useful in statistical applications will be emphasized.
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The theory of parametric and nonparametric tests of hypotheses is examined. Criteria for suitability of parametric or nonparametric methods are developed for various statistical models. These include one-sample tests for location, two-sample tests for location (related and independent), two-sample tests for dispersion, and k-sample tests for location (related and independent). In addition, tests for association, tests for randomness, tests for goodness of fit, and methods of paired comparisons are developed. Applications will be made to the behavioral, social, and life sciences.
Score: 8.198331 Details | Listing | Web page
This course is a hands-on introduction to modern computer-intensive statistical methods, such as the bootstrap and Monte Carlo methods. It will be taught on platforms, such as Microsoft Excel and Visual Basic for Applications (VBA). Emphasis will be placed on fundamental programming rather than the use of canned routines. Applications will be taken from the fields of finance and accounting.
Score: 8.198331 Details | Listing | Web page
This course focuses on the use of the SAS statistical package for the solution of practical business problems. Emphasis will be placed on hypothesis testing procedures and model building using SAS with applications to various business functions.
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The principles and applications of experimental design are covered. Completely randomized block design, Latin squares, balanced and nonorthogonal factorial experiments, and hierarchical designs are covered. Nested and nested-factorial experiments are included. Such additional topics as linear contrasts, orthogonal polynomials, and multiple comparisons are covered. Various applications in the fields of business and science are studied.
Score: 8.198331 Details | Listing | Web page
This course will cover statistical models for time series decomposition, linear and nonlinear trends, spectral methods, data smoothing methods, and forecasting models.
Score: 8.198331 Details | Listing | Web page
Special topics in statistics will be studied during the semester. Reports will be presented, and written papers will be required in several areas. Topics may be chosen from among the following areas: quality and productivity methods, linear models, multivariate methods, nonparametric methods, and survey sampling.
Score: 8.198331 Details | Listing | Web page
Offered to students in their junior or senior year who wish to pursue independent study or research in an area or topic not covered in course offerings.
Score: 8.198331 Details | Listing | Web page
This course offer students of superior academic achievement the opportunity to work individually with a professor on a major research project in CIS/STA/QMM. This project involves in-depth and original research of an empirical and/or theoretical question in the area of focus. The degree "with honors" is conferred upon acceptance of the honors project by the College Honors Committee.
Score: 8.198331 Details | Listing | Web page
This course offer students of superior academic achievement the opportunity to work individually with a professor on a major research project in CIS/STA/QMM. This project involves in-depth and original research of an empirical and/or theoretical question in the area of focus. The degree "with honors" is conferred upon acceptance of the honors project by the College Honors Committee.
Score: 8.198331 Details | Listing | Web page
An introduction to applied statistics and statistical computing. Hands-on data analysis. Graphical inference. The five number summary, box plots, scatterplots, normal probability plots. Elementary probability. Statistical estimation and hypothesis testing. Linear regression. Students are expected to analyze real data sets and write reports.
Score: 8.198331 Details | Listing | Web page
Combinatorics, discrete probability, random walks, and game theory. Emphasis on model building.
Score: 8.198331 Details | Listing | Web page
Sampling, estimation, tests of hypotheses, including one- and two-sample t-tests, two- and three-way tables for nominal and ordinal data, linear regression, analysis of variance through two-way with interaction, appropriate statistical software.
Score: 8.198331 Details | Listing | Web page
Familiarity with the Windows computing environment encouraged. Analysis of variance, simple and multiple regression, nonparametric statistics, statistical model building.
Score: 8.198331 Details | Listing | Web page
A second course in probability and statistics and the evaluation of evidence in the Forensic Sciences. A study of discrete probability and statistics with particular applications to law, medicine and forensic sciences.
Score: 8.198331 Details | Listing | Web page
See Department for Description.
Score: 8.198331 Details | Listing | Web page
Combinatorics, distribution theory for discrete and continuous random variables, central limit theorems.
Score: 8.198331 Details | Listing | Web page