Geostatistics models continuous variation over geographical space. It is widely used for spatial interpolation. We will use R and ArcGIS to explore and develop an understanding of variogram and kriging and how they can be used for robust and unbiased interpolation of data over space. Depending on the interests of the participants, the workshop will […]
This workshop will cover introductory concepts, tools, and techniques to model spatially referenced data observed over a regular or irregular grid. We will cover models such as spatial autoregressive that emphasizes the idea of spatial correlation via neighborhood. The workshop will focus on conceptual aspects, diagnostics tests, implementation in R, and interpretation of results.
This workshop will cover techniques for conducting large-scale inference, using Python and its libraries. We will cover the principals of how large scale inference is different from classical inference, and why multiple comparisons should usually be accounted for in an analysis. We will then discuss the Bonferroni method, Benjamini and Hochberg's False Discovery Rate (FDR), […]
This workshop will cover introductory GIS concepts, tools, and techniques. We will use QGIS (and ArcGIS) and learn the basics of vector- and network-data GIS by solving problems. You will also learn to generate maps. We will use the graphical user interface and no programming experience is required for this workshop. The workshop is meant for […]
Simulation studies are used to understand the properties of statistical procedures. This technique is widely used when conducting power analysis for research studies, which is one focus for this workshop. Simulation studies can also be used to assess the performance of statistical procedures in non-standard settings, such as when there are concerns about missing data, […]
This lecture-format workshop will present an overview of available modes and methods of survey data collection as well as an introduction to the survey response process and implications for questionnaire design. Participants will gain an appreciation of the tradeoffs inherent in survey design decisions and how design can affect data quality and survey errors. Topics […]
This workshop will cover dimension reduction techniques for data analysis in Python, focusing on Principal Component Analysis (PCA), factor analysis, and canonical correlation analysis (CCA). Depending on audience interest, dimension reduction regression (e.g. SIR), and kernel versions of the classical dimension reduction techniques can also be discussed. The presentation will begin by covering the conceptual […]
This semester, a series of seven workshops was offered by CSCAR illustrating various approaches to statistical analysis using the Python Statsmodels package. In this session, we will provide an open forum for people to get hands-on assistance in this environment. Several data sets and suggested analysis goals will be provided, but participants are also welcome […]
Missing data arise in many fields of research, and a large body of statistical tools has been developed to facilitate statistical analysis in the presence of missing data. Here we focus mainly on multiple imputation, which is a broadly-applicable approach for working with missing data. We will illustrate through several case studies how multiple imputation […]
This workshop will cover GIS concepts and techniques for analyzing geometric networks embedded in geographical space. We will mainly focus on road network, but the ideas and techniques apply to similar network such as water and electric distribution and gas pipelines. We will primarily use open source tools in R and QGIS, but will also […]