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.
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.
Prerequisite: 01:640:115 or permission of department. Credit not given for more than one of the following: 01:960:201, 211, 321 and 01:220:201.
Introduction to statistical inference, including descriptive statistics, probability, sampling, estimation, hypothesis testing, and simple regression analysis. Instruction in the use of computer packages.
Prerequisite: 01:640:115 or permission of department. See Level II Statistics restrictions. Credit not given for more than one of 01:960:201, 211, 285, and 401; nor for more than one of 01:960:212, 380, 384, 401, and 484.
Principles and methods of statistics, including probability distributions, sampling, estimation, hypothesis testing, regression and correlation analysis, curvefitting, nonparametric methods, and analysis of variance (ANOVA)
Prerequisites: 01:640:115 or equivalent. Credit not given for more than one of 01:960:201, 211, 285, and 401.
Topics include descriptive statistics, probability theory, random variables, sampling distributions, estimation, hypothesis testing, and one and twosample ttests.
Prerequisites: 01:640:115 and 01:198:142/01:960:142
Introduction to probability and statistics underlying data science. Topics include regression, resampling, confidence intervals, hypothesis testing, and related probability distributions
Prerequisites: 01:198:142/01:960:142 or Level II Statistics
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 of R or programming is required. Interactions with databases will be included.
Prerequisite: 01:960:211 or 321, or equivalent.
Modern data analysis and applied statistical decision theory in such fields as market research, business forecasting, and operations research. Analysis of time series and index numbers.
Prerequisite: Level II Statistics and 01:640:152
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.
Prerequisite: One term of calculus.
Descriptive statistics; elementary probability theory; probability distributions; the binomial, Poisson, exponential and normal distributions; basic sampling theory; applications of probability theory.
Prerequisite: O1:640:251. Credit not given for both 01:960:381 and 01:640:477.
Probability distributions; the binomial, geometric, exponential, Poisson, and normal distributions; moment generating functions; sampling distributions; applications of probability theory.
Prerequisite: 01:960:381 or equivalent. Credit not given for both 01:960:382 and 01:640:481.
Statistical inference methods, point and interval estimation, maximum likelihood estimates, information inequality, hypothesis testing, NeymanPearson lemma, linear models.
Prerequisite: one of the following courses: 960:201, 211, 285, 379, 381 or permission of department. Credit not given for more than one of 01:960:212, 384, 401 or 484.
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 crossclassified data, chisquare tests.
*960:384 is numbered 960:380 previously. Credit is not given for both 960:380 and 384.
Registration limited to students in the Graduate School of Social Work. Graded as satisfactory or unsatisfactory.
Descriptive statistics; methods of classifying and summarizing data; estimation and prediction; correlation and regression analysis; principles of hypothesis testing
Fiveweek course; 3 hrs. lecture and lab.
Prerequisite: Level II Statistics.
Introduction to the use of statistics computer packages with main focus on SAS. Includes: generating random samples, estimation, testing hypotheses. ANOVA
Prerequisite: Caclulus I, or permission of the department.
Corequisite: Calculus II.
Lectures and discussions of real life examples or case studies on statistics and probability theory, and their ramifications. Topics may vary term by term. Extensive data analysis required.
Prerequisite: 01:640:115 or equivalent. Credit not given for more than one of 01:960:201, 211, 285, and 401; nor for more than one of 01:960:212, 384, 401, and 484.
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: Level II Statistics.
Multiple and nonlinear correlation and regression techniques for analysis of events in time and space: analysis of variance and covariance (ANOVA), related multivariate techniques, response surface approaches.
Prerequisite: Level II Statistics or permission of department.
Introduction to the methodology of multivariate analysis. Multiple linear regression, discriminant analysis, profile analysis, canonical correlation, principal components, and factor analysis.
Prerequisite: 01:960:379 or 381 or equivalent or permission of department.
Principles of sampling application for economic procurement or assessment of data. Current techniques for area sampling, sampling of accounts, largescale surveys, stratification, cluster sampling, systematic sampling, twostage sampling, and construction estimates.
Prerequisite: Level II Statistics.
Statistical measures; histogram analysis; construction and analysis of control charts for variables and attributes; analysis of capability indices; acceptance sampling plans; statistical aspects of tolerance; analysis of means.
Prerequisite: One of the following coures: 960:201, 211, 285, 379, 381 or an equivalent course in basic probability theory. See credit restrictions for Level II Statistics.
Estimation, hypothesis testing, chisquare methods, correlation and regression analysis, basis of design of experiments.
Prerequisite: Level II Statistics.
Use of various computerbased techniques, including graphical, to understand and interpret data. Exposure to basic analysis of categorical, timeseries and multivariate data in the applied areas such as biostatistics, quality control and others.
Prerequisite: Level II Statistics.
Principles of designs. Nature and analysis of various designs; randomized blocks, Latin squares, factorial designs. Applications to specific problems.
Prerequisites: 01:640:251 and Level II Statistics.
A survey of current theory and practice in this field.
Prerequisite: Permission of department.
501 Hill Center 

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