By Hamish McCallum
Presents transparent information on tips to estimate parameters for types of animal populations. Concentrates on how you can choose the simplest method of parameter estimation for a specific challenge, and the way to make sure that the amount estimate is the fitting one for the categorical goal of the modelling workout. Softcover.
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Additional info for Population Parameters: Estimation for Ecological Models
However, the maximum lies on the top of a rather ﬂat ridge. This means that a variety of other combinations of the parameters will ﬁt the data almost as well. 8) numerically. 5 shows how to do it using a standard spreadsheet such as Excel. In general, the process of maximum likelihood estimation of a parameter set β, given a set of observed data y can be summarized as follows: 1 Obtain a function that gives the joint probability of any particular set of outcomes y, given known values of the parameters β.
That cannot multiply rapidly within their hosts) may have on the population size of their hosts. The primary objective of the model was to investigate how the extent to which parasites can depress the population size of their host depends on the sizes of the various parameters that describe the host–parasite interaction. This example is selected to illustrate two important points. First, it shows how changing the variables in a model may lead to a form that is simpler and easier to understand. Second, it shows how examination of the algebraic forms of modelling results can be used to perform a ‘sensitivity analysis’, to determine which parameters or parameter combinations are most important in the outcome of the model.
3 Using a t test for estimation Student’s t test is usually the ﬁrst hypothesis test taught to students of statistics. Its purpose is to test whether two treatments (or a treatment and control) differ in their effect on the mean of some response. Alternatively, it can be used to test if two samples could have been drawn from populations with the same mean. More formally, it tests H0: μ1 = μ2 versus HA: μ1 ≠ μ2. The standard test statistic is t= [1 − [2 , se [1 − [2 where [1 and [2 are the means of the response for treatments 1 and 2, and se [1 − [2 is the standard error of the difference between the treatment means.