Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing. Basic algebra and familiarity with computer and internet necessary. (Gen.Ed. R1, R2)

Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing. Basic algebra and familiarity with computer and internet necessary. (Gen.Ed. R1, R2)

Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing. Basic algebra and familiarity with computer and internet necessary. (Gen.Ed. R1, R2)

Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing. Basic algebra and familiarity with computer and internet necessary. (Gen.Ed. R1, R2)

Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing. Basic algebra and familiarity with computer and internet necessary. (Gen.Ed. R1, R2)

Designed for students in the social science and business related fields of study. Introduction to basic statistical methods used to collect, summarize, and analyze numerical data. Emphasis on application to decision making; examples from the social sciences and business. Topics include: common statistical notation, elementary probability theory, sampling, descriptive statistics, statistical estimation and hypothesis testing. Basic algebra and familiarity with computer and internet necessary. (Gen.Ed. R1, R2)

Economics of environmental quality and natural resource management; theory of externalities, public goods, and resource extraction. Benefit-cost analyses of natural resource use and preservation of unique resources.

Basic concepts in regression analysis and econometric methods. Application of methods to real world data; emphasis on application through use of econometric software and microcomputers. Computer labs required. Students undertake research projects.

Intermediate level microeconomic theory. Consumer demand theory and economics of production. Geometric and mathematical approaches. Models of market behavior, related to examples. Applications to business and government decision making emphasized. Required course for more advanced departmental offerings. Prerequisite: RES-ECON 102 or ECON 103 and MATH 127 or 131.

Emphasis on understanding and interpreting statistical information, and using it to form sound judgments in business situations. Includes brief review of sampling, sampling distributions, and basic inference for one-sample situations; comparison of two populations, chi-square tests for contingency tables, analysis of variance, simple linear regression, and topics in multiple regression and time series. Statistical software used for computations. A project using regression methods required.