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ENVS 355

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

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ENVS 355

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  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 • 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

  6. 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

  7. 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

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

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

  10. 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

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

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