SPSS I Introduction to SPSS

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Note: Topic order is subject to change.

This workshop is designed to introduce participants to SPSS. It will cover the fundamentals of SPSS, within-case transformations, data management with multiple files, and basic statistics and graphics. Useful for any scholar engaged in quantitative research.

Fundamentals

This portion introduces SPSS, the menu and the help systems, and the three main types of files used.  It then addresses defining variables, attaching labels, defining missing values, and various ways to enter data into SPSS.  Finally, it covers a brief introduction to obtaining frequency distributions, descriptive statistics, and cross tabulations of variables.

Within-Case Transformations

This portion introduces data management capabilities, including recoding variables (manual and automatic), computing new variables using formulas, and counting occurrences of values within subjects.  Attention then turns to temporary transformations, conditional processing of transformations, and repetitive transformations.

Data Management with Multiple Files

This portion begins with a discussion of subsetting data files by drawing samples, selecting groups and excluding groups from analysis.  Then, the two main methods of merging SPSS data files are covered: adding additional variables and adding additional cases.

Basic Statistical Analysis

The portion includes a brief demonstration of a statistical analysis in SPSS. While not delving deep into statistical theory, we will cover the basics of an analysis, as well as discuss the graphing facilities in SPSS.

Registration

To register for CSCAR Workshops, call the CSCAR front desk at (734) 764-7828 or come to the office in person with cash or check or a UM 6-digit department shortcode:

OFFICE HOURS

9:00 a.m. – 5:00 p.m., Monday through Friday
Closed 12pm – 1:00 p.m. every Tuesday for staff meeting.
Voice: (734) 764-7828 (4-STAT from a campus phone)
Fax: (734) 647-2440

ADDRESS

Center for Statistical Consultation and Research (CSCAR)
The University of Michigan
3550 Rackham
915 E. Washington St.
Ann Arbor, MI 48109-1070

 

Python for Data Analysis

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This workshop, held over two half-days, will provide participants with an overview of pandas; a Python library for data analysis. The workshop will focus on pandas; a Python library for data analysis. There will be both lecture and hands-on computer work using Python (via Spyder IDE and IPython notebook) on real datasets (crash data will be used). Topics will include how to load in different data formats (csv, excel, access database, etc.), clean, manipulate, process, and crunch data, graph results (using matplotlib and bokeh) and write results to an output file. Computers will be provided but participants are encourage to bring their own laptops and datasets to work with using pandas. Call (734) 764-7828 to register.

 

Data management and analysis with Python and Pandas

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Pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Topics will include how to read various dat formats (csv, excel, databases, etc), clean, manipulate, analyze, graph and write results to an output file. Real world data will be used. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

Registration  http://ttc.iss.lsa.umich.edu/ttc/sessions/data-management-and-analysis-with-python-and-pandas/

Geospatial analysis with Python

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This workshop will cover basic geospatial analysis in Python. Topics covered will include reading and writing various GIS file formats (shapefile, KML, geojson, csv), geocoding, common geometric operations like finding closest line to a point, point in polygon, spatial indexing and spatial joins etc. The workshop will focus solely on vector data (points, lines, polygons).  The will be mostly accomplished using the Python modules: fiona, shapely, rtree (but not arcpy).

Geospatial image processing with Google Earth Engine

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Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. This class will give a gentle introduction to the power and convenience of Earth Engine. Basic knowledge of remote sensing (or raster data) and computer programming (in any language) is required. I will explain more substantive concepts as we work through examples. You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine. We will use the web-based IDE for the Earth Engine JavaScript API, but if you want to use the Python API please see the instructions at earth engine to install and activate it.

Introduction to SPSS

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This workshop is 2 sessions

Audience: Never before SPSS users who will be using SPSS for Windows.  Those using SPSS for Unix or Macintosh should email the instructor at jerrick@umich.edu before enrolling.

Note: Topic order is subject to change.  Participants must sign up for the entire series.

Fundamentals

This portion introduces SPSS for Windows, the menu and the help systems, the three main types of files used, and printing from within SPSS.  It then addresses defining variables, attaching labels, defining missing values, and various ways to enter data into SPSS.  Finally, it covers a brief introduction to obtaining frequency distributions, descriptive statistics, and cross tabulations of variables.

Within-Case Transformations

This portion introduces data management capabilities, including recoding variables (manual and automatic), computing new variables using formulas, and counting occurrences of values within subjects.  Attention then turns to temporary transformations, conditional processing of transformations, and repetitive transformations.  SPSS syntax is also introduced.

Data Management with Multiple Files

This portion begins with a discussion of subsetting data files by drawing samples, selecting groups and excluding groups from analysis.  Then, the two main methods of merging SPSS data files are covered: adding additional variables and adding additional cases.  Next, creating aggregated data sets and applying aggregated data to individuals is covered.  Lastly, importing and exporting data between SPSS and other statistical programs (Excel, dBase, SAS) is demonstrated.

Basic Statistics and Graphics

This portion covers basic exploratory procedures, including obtaining percentiles, frequencies, descriptive statistics, and cross tabulations. Basic comparative procedures including two-sample t-tests, paired t-tests, and one-way analysis of variance are also covered.  Then, simple bivariate correlation analysis is introduced.  Participants are given a basic introduction to commonly used graphical procedures for displaying data, including scatter plots, bar graphs, histograms, and boxplots.

Registration

To register for CSCAR Workshops, call the CSCAR front desk at (734) 764-7828 or come to the office in person with cash or check or a UM department shortcode:

OFFICE HOURS

9:00 a.m. – 5:00 p.m., Monday through Friday
Closed 12pm – 1:00 p.m. every Tuesday for staff meeting.
Voice: (734) 764-7828 (4-STAT from a campus phone)
Fax: (734) 647-2440

ADDRESS

Center for Statistical Consultation and Research (CSCAR)
The University of Michigan
3550 Rackham
915 E. Washington St.
Ann Arbor, MI 48109-1070

 

New private insurance claims dataset and analytic support now available to health care researchers

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The Institute for Healthcare Policy and Innovation (IHPI) is partnering with Advanced Research Computing (ARC) to bring two commercial claims datasets to campus researchers.

The OptumInsight and Truven Marketscan datasets contain nearly complete insurance claims and other health data on tens of millions of people representing the US private insurance population. Within each dataset, records can be linked longitudinally for over 5 years.  

To begin working with the data, researchers should submit a brief analysis plan for review by IHPI staff, who will create extracts or grant access to primary data as appropriate.

CSCAR consultants are available to provide guidance on computational and analytic methods for a variety of research aims, including use of Flux and other UM computing infrastructure for working with these large and complex repositories.

Contact Patrick Brady (pgbrady@umich.edu) at IHPI or James Henderson (jbhender@umich.edu) at CSCAR for more information.

The data acquisition and availability was funded by IHPI and the U-M Data Science Initiative.

Fourier transform and its applications in data analysis

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Spectral decomposition of time series (1-D) and image (2-D) data is a commonly used technique across various disciplines that use sensors for data collection. Fourier analysis is the foundation of spectral decomposition methods and provides basis (and intuition) for the more advanced methods in time-frequency analysis such as wavelets and Wigner-Ville decomposition. This workshop will cover 1-D Fourier transform with applications to signals and time series data and will also provide a flavor of applications in image processing.

Image processing III

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If you use image data in your work, but are not trained to analyze it, this workshop could be for you. This is the third workshop and will build upon the material covered in the two previous workshops last semester. We will cover texture analysis, Hough transform, and frequency domain methods.

If you are not exposed to Fourier analysis consider attending the CSCAR workshop Fourier transform and its applications in data analysis’.