Statistics Courses | St. Lawrence University Math, Computer Science, and Statistics

Statistics Courses

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STAT 113 Applied Statistics An introduction to statistics with emphasis on applications. Topics include the description of data with numerical summaries and graphs, the production of data through sampling and experimental design, techniques of making inferences from data such as confidence intervals and hypothesis tests for both categorical and quantitative data. The course includes an introduction to computer analysis of data with a statistical computing package.  Fulfills the distribution requirement in Quantitative Literacy (QLR).  Offered every semester.

STAT 201 Statistical Computing
This 0.5 unit, half semester class, provides an introduction to the core ideas of programming and modern statistical computing through the use of the statistical programming language and software, R. Assumes no prior knowledge of programming but a familiarity with basic statistics is required. The course focuses on building essential programming skillsas well as introduce students to basic data science skills such as data wrangling and visualization. Prerequisite: Statistics 113 or permission of instructor. No longer offered. Interested students should instead take STAT 234 - Introduction to Data Science.

STAT 213 Applied Regression Analysis A continuation of STAT 113 intended for students in the physical, social or behavioral sciences. Topics include simple and multiple linear regression, model diagnostics and testing, residual analysis, transformations, indicator variables, variable selection techniques, logistic regression and analysis of variance. Most methods assume use of a statistical computing package. Prerequisite: STAT 113.  Offered every semester.

STAT 226 Statistical Methods of Data Collection An introduction to the statistical design and analysis of experiments. This course covers the basic elements of experimental design, including randomization, blocking and replication. Topics will include completely randomized design, randomized complete block design Latin Square and fractorial designs. Analysis of variance techniques for analyzing data collected using these methods is extensively discussed. Additional topics in survey sampling are covered as time allows. Thorough use of statistical software package is incorporated. Prerequisite: STAT 113. Typically offered in the Fall semesters of even number years.

STAT 234 Introduction to Data Science .An introduction to fundamental data science concepts using modern statistical programming languages and software. The course assumes no prior knowledge of programming but a familiarity with basic statistics is required. The course focuses on building essential data science skills such as data manipulation and visualization, basics of programming, string manipulation, and modern data sources (such as web and databases). Emphasis will be placed on building skills applicable to large scale projects. Students may receive credit for only one of STAT 234, STAT 3007, or STAT 201. This course fulfills the Computing Requirement of the Statistics Major. Pre-reqs: STAT-113 or ECON-200 or FRPG-2052

STAT 240 Topics in Statistical Learning This course is intended to introduce students to a variety of tools and methods in statistical and machine learning. Topics may include unsupervised learning (i.e., cluster analysis), dimensionality reduction, and supervised learning (via classification algorithms). The emphasis will be on applying procedures to data and interpreting both numerical and graphical results. Prerequisite: Statistics 201 and one of Statistics 213, 226 or 342, or permission of instructor. Last offered Fall 2017

STAT 325 Probability This course covers the theory of probability and random variables, counting methods, discrete and continuous distributions, mathematical expectation, multivariate random variables, functions of random variables and limit thorems. Prerequisites Math 205. Also offered through Mathematics as MATH 325. Offered in the fall semester.

STAT 326 Mathematical Statistics This course deals with the theory of parameter estimation, properties of estimators, and topics of statistical inference including confidence intervals, tests of hypotheses, simple and multiple linear regression, and analysis of variance. Prerequisite: STAT 325. Offered in the Spring semester.

STAT 342 Econometrics A study of statistical techniques economists have found useful in analyzing economic data, estimating relationships among economic variables, and testing economic theories. Topics include multiple regression probit and logit analysis, heteroscedasiticity, autocorrelation, and simultaneous equations models. Prerequisites: ECON 200, 251, 252. Also offered as Economics 342. 

STAT 343 Time Series Analysis Statistical methods for analyzing data that vary over time are investigated. Topics include forecasting systems, regression methods, moving averages, exponential smoothing, seasonal data, analysis of residuals, prediction intervals and Box-Jenkins models. Application to real data, particularly economic data, is emphasized along with the mathematical theory underlying the various models and techniques. Prerequisite: Math 136. 

STAT 389/390 Independent Projects Permission required.

STAT 450 SYE: Senior Seminar Permission required.

STAT 489 SYE: Senior Project for Majors Permission required.

STAT 498 SYE:  Senior Honors Project For Majors Permission required.