review1 - 1 Build a model from basic assumptions That is...

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Review for Exam 1 Exam Conditions This will be an in-class, timed exam. You will not work in groups. Expect to see roughly half a dozen problems or questions similar to things we have done in class or on graded homework, but not computationally intensive. This means about 12-15 minutes per problem. Some problems might have multiple parts. You can use any books or notes that you like. A hand held calculator is allowed (up to TI-92 in terms of features), but a laptop or other computer is not. You will probably have to sketch some graphs. Bring graph paper and whatever drawing tools you like to use. Exam Content Generally speaking, anything that I deem similar to problems you have done on worksheets or graded homework, provided it does not require computing machinery to get the answer. One good exercise for you would be to gather all your old worksheets and graded assignments and then categorize each question as “requires computing machine” or “does not”. More speciFcally, you must be able to:
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Unformatted text preview: 1. Build a model from basic assumptions. That is, given one or more populations, birth rate, death rate, and/or any other information, write di±erence equation(s) for the population(s). You should be able to do this for one or more population variables. You should be able to write the model as either di±erence equation(s) or as recursive equation(s). 2. Conduct qualitative analysis on a one-variable model. I.e.: • ²ind all equilibrium solutions. • Predict behavior of other solutions. • Use your analysis to answer questions about the model or to chose from among models. (The matching problem from Homework 2 is an example.) 3. Conduct qualitative analysis of a two-variable model. I.e.: • Write the model as a recursive matrix equation. • ²ind eigenvalues and eigenvectors of the coe³cient matrix. • Use the eigenvalues and eigenvectors to predict the behavior of solutions. • Use your analysis to answer questions about the model or to chose from among models. 1...
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