1. ENVS 355 Data, data, data Models, models, models Policy, policy, policy

2. In an Ideal world: Good Data Informs model Interrogate model Refined Evolving Model 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 DETE CT THIS

3.  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

4.  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

5. 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

6. 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

7. 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

8. 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

9. 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

10.  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

11. 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