Data Analysis for Sustainability

Catalog description

This course will introduce students to the study and practice of data analysis in sustainability and expand on core concepts from probability and statistics. Analysis methods presented will be applied to datasets ranging from energy consumption at the household and commercial building levels, to other national datasets, such as the national household travel survey (NHTS) and pollutant or vehicle emissions data from the EPA. Topics include (i) numerical and graphical summaries of data, (ii) hypothesis testing, (iii) confidence intervals, (iv) counts and tables, (v) analysis of variance, (vi) regression, (vii) principal components, and (viii) cluster analysis. Additionally, topics in data collection and survey design will also be introduced. These include: (a) sampling and weighting; (b) questionnaire design; and (c) design of experiments. At the end of this course, students should be able to think critically about data and apply standard statistical inference procedures to draw conclusions from such analyses. This course will be computationally, not mathematically, intensive and will use the R language and environment for statistical computing and graphics.