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

2. Environmental Problems 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

3. Course Goals 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

4. 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 problem solve in a collaborative way

5. Probable Topics Predator-Prey Relations and statistical equilibrium Predator-Prey Relations and statistical equilibrium Population projects and demographic shifts Population projects and demographic shifts Measuring global and local climate change Measuring global and local climate change Resource depletion issues and planning Resource depletion issues and planning Indicators of potential large scale climate change Indicators of potential large scale climate change Vehicle Mix in Eugene Vehicle Mix in Eugene

6. More Goals of this Course To gain practice in how to frame a problem To gain practice in how to frame a problem To practice making toy models involving data organization and presentation To practice making toy models involving data organization and presentation To understand the purpose of making a model 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 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 Provide you with a mini tool kit for analysis

7. Sequence for Environmental Data Analysis Conceptualization of the problem  which data is most important to obtain Conceptualization of the problem  which data is most important to obtain Methods and limitations of data collection  know your biases Methods and limitations of data collection  know your biases Presentation of Results => data organization and reduction; data visualization; statistical analysis Presentation of Results => data organization and reduction; data visualization; statistical analysis Comparing different models Comparing different models

8. Some Tools Linear Regression  predictive power lies in scatter Linear Regression  predictive power lies in scatter Slope errors are important Slope errors are important Identify anomalous points by sigma clipping (1- cycle) Identify anomalous points by sigma clipping (1- cycle) Learn to use the regression tool in Excel Learn to use the regression tool in Excel Least squares method used for best fit determination Least squares method used for best fit determination

9. More Tools Chi square test Chi square test Understand how to determine your expected frequencies Understand how to determine your expected frequencies Two chi square statistic requires marginal sum calculations Two chi square statistic requires marginal sum calculations Chi square statistic used to accept or reject the null hypothesis Chi square statistic used to accept or reject the null hypothesis Know how to compute it Know how to compute it

10. Estimation Techniques Extremely useful skill  makes you valuable 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? 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 Scale from familiar examples when possible Perform a reality check on your estimate Perform a reality check on your estimate

11. Yet More Tools Comparing statistical distributions to see if they are significantly different Comparing statistical distributions to see if they are significantly different Higher order tests (KS test – most powerful of all – very seldom used) Higher order tests (KS test – most powerful of all – very seldom used) Discrete or arrival statistics (Poisson Statistics) Discrete or arrival statistics (Poisson Statistics) Data visualization  very useful Data visualization  very useful