Economic analysis of environmental problems focusing on air, water, and land pollution. Emphasis is on analyzing the individual incentives that lead to environmental degradation, the valuation of environmental quality, and the design and evaluation of regulations that seek to improve environmental quality. Includes the economic analysis of global climate change. (Gen.Ed. SB)

This course covers methods of sample-based estimation and inference. Topics include hypothesis testing for two populations, analysis of variance for comparing three or more populations, simple linear regression, topics in multiple regression, and univariate time-series techniques (if time permits) such as moving averages and exponential smoothing. Statistical software is used for advanced computations. Basic algebra required.

This course covers methods of sample-based estimation and inference. Topics include hypothesis testing for two populations, analysis of variance for comparing three or more populations, simple linear regression, topics in multiple regression, and univariate time-series techniques (if time permits) such as moving averages and exponential smoothing. Statistical software is used for advanced computations. Basic algebra required.

This course covers methods of sample-based estimation and inference. Topics include hypothesis testing for two populations, analysis of variance for comparing three or more populations, simple linear regression, topics in multiple regression, and univariate time-series techniques (if time permits) such as moving averages and exponential smoothing. Statistical software is used for advanced computations. Basic algebra required.

This course covers methods of sample-based estimation and inference. Topics include hypothesis testing for two populations, analysis of variance for comparing three or more populations, simple linear regression, topics in multiple regression, and univariate time-series techniques (if time permits) such as moving averages and exponential smoothing. Statistical software is used for advanced computations. Basic algebra required.

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.

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.

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.

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.

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.