ENVS 355ENVS 355Data, data, data

Models, models, modelsPolicy, policy, policy

In an Ideal world:In an Ideal world:

Good DataInforms model

Interrogate model

Refined EvolvingModel

Data Based Policy Real world behaves

better

Failure Points in this Process

BAD, Biased, or Incomplete

Data Biased Model

Data Ignored; Bias and Anecdotes Abound

STOP;

MUST

DETECT

THIS

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

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 ContentCourse 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

• 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

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