# Course Synopses

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.

*Not currently offered.*

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.

* Generally offered fall and spring semesters.*

Principles and methods of statistics, including probability distributions, sampling, estimation, hypothesis testing, regression and correlation analysis, curve-fitting, 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.

*Generally offered fall and spring semesters.*

Topics include descriptive statistics, probability theory, random variables, sampling distributions, estimation, hypothesis testing, and one- and two-sample t-tests.

Prerequisites: 01:640:115 and 01:198:142/01:960:142

*Generally offered fall and spring semesters.*

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

*Generally offered spring semester only.*

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.

* Not currently offered.*

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

*Generally offered fall semester only.*

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.

*Generally offered fall and spring semesters.*

Descriptive statistics; elementary probability theory; probability distributions; the binomial, Poisson, exponential and normal distributions; basic sampling theory; applications of probability theory.

Prerequisite: 01:640:251. Credit not given for both 01:960:381 and 01:640:477.

*Generally offered fall semester only.*

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.

* Generally offered spring semester only.*

Statistical inference methods, point and interval estimation, maximum likelihood estimates, information inequality, hypothesis testing, Neyman-Pearson 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.

*Generally offered fall and spring semesters.*

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.

*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.

* Not currently offered.*

Descriptive statistics; methods of classifying and summarizing data; estimation and prediction; correlation and regression analysis; principles of hypothesis testing

Five-week course; 3 hrs. lecture and lab.

Prerequisite: Level II Statistics.

*Generally offered fall and spring semesters. *

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.

Co-requisite: Calculus II.

* Offered irregularly.*

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.

*Generally offered fall and spring semesters.*

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.

*Generally offered fall and spring semesters.*

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.

*Generally offered spring semester only.*

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.

*Generally offered spring semester only.*

Principles of sampling application for economic 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 estimates.

Prerequisite: Level II Statistics.

* Not currently offered during the academic year; may be offered in the summer.*

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 courses: 960:201, 211, 285, 379, 381 or an equivalent course in basic probability theory. See credit restrictions for Level II Statistics.

*Generally offered fall and spring semesters.*

Estimation, hypothesis testing, chi-square methods, correlation and regression analysis, basis of design of experiments.

### Formerly known as Computing and Graphics in Applied Statistics

Prerequisite: Level II Statistics.

*Generally offered fall and spring semesters.*

Use of various computer-based techniques, including graphical, to understand and interpret data. Exposure to basic analysis of categorical, time-series and multivariate data in the applied areas such as biostatistics, quality control and others.

Prerequisite: Level II Statistics.

*Generally offered spring semester only.*

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.

* Not currently offered.*

A survey of current theory and practice in this field.

Prerequisite: Permission of department.