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ENVS 355
Data, data, data
Models, models, models
Policy, policy, policy
In an Ideal world:
BAD, Biased, or
Good Data
Incomplete Data
Informs
Biased Model
model
Interrogate
model
Refined
Evolving
Model
DataBased
Ignored;
Bias
Data
Policy
and
Real
world
Anecdotes
behaves
Abound
better
Failure Points in this Process
STOP;
MUST
DETE
CT
THIS
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Usually characterized by noisy/ambiguous
data which can then support multiple views
of the same problem  Who’s right?
Difficult to model due to a) poor data
constraints and b) missing information
The scientific method is usually not part of
environmental policy
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To give students experience in these three
intertwined difficulties
To develop student data analysis and
presentation skills so that you can become
worthwhile in the real world
To learn how to use a computer to assist you
in data analysis and presentation
To give students experience in project
reporting
MORE GOALS OF THIS COURSE
• To gain practice in how to frame a problem
• To practice making toy models involving data
organization and presentation
• To understand the purpose of making a model
• To understand the limitations of modeling and that
models differ mostly in the precision of predictions
made
• Provide you with a mini tool kit for analysis
Course Content
• Introduction to various statistical tools,
tests for goodness of fit, etc.
• To understand sparse sampling and
reliable tracers
• To construct models with predictive power
and to assess the accuracy of those
models
• To learn to scale in order to problem solve
on the fly
PROBABLE TOPICS
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Predator-Prey Relations and statistical equilibrium
Population projects and demographic shifts
Measuring global and local climate change
Resource depletion issues and planning
Indicators of potential large scale climate change
Vehicle Mix in Eugene
SEQUENCE FOR ENVIRONMENTAL
DATA ANALYSIS
• Conceptualization of the problem  which data is
most important to obtain
• Methods and limitations of data collection  know
your biases
• Presentation of Results => data organization and
reduction; data visualization; statistical analysis
• Comparing different models
SOME TOOLS
• Linear Regression  predictive power lies in scatter
 your never told this!
• Slope errors are important  your never told this
either!
• Identify anomalous points by sigma clipping (1
cycle)
• Learn to use the regression tool in Excel
• Graph the data always  no Black Boxes
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Chi square test – is your result different than
random?
Chi square statistic - Know how to compute it
and what it means
Comparing statistical distributions to detect
significant differences
Advanced Methods (KS Test  most powerful
but not widely used)
Discrete/arrival statistics (Poisson statistics)
Data visualization  very important
ESTIMATION TECHNIQUES
• Extremely useful skill  makes you valuable
• Devise an estimation plan  what factors do you
need to estimate  e.g. how many grains of sand
are there in the world?
• Scale from familiar examples when possible
• Perform a reality check on your estimate