1 / 11

ENVS 355

ENVS 355. Data, data, data Models, models, models Policy, policy, policy. In an Ideal world:. STOP; MUST DETECT THIS. Good Data. BAD, Biased, or Incomplete Data. Informs model. Biased Model. Interrogate model. Refined Evolving Model. Data Based Policy  Real world behaves better.

elaina
Download Presentation

ENVS 355

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. In an Ideal world: STOP; MUST DETECT THIS Good Data BAD, Biased, or Incomplete Data Informs model Biased Model Interrogate model Refined Evolving Model Data Based Policy  Real world behaves better Data Ignored; Bias and Anecdotes Abound Failure Points in this Process

  3. 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

  4. 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

  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. More Tools • 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

More Related