![]() The smaller the sum the better the data fit the predicted curve. To check the predicted fit of the line one usually calculates all the residuals (observed – predicted) and sums all the differences. your data) and the predicted values (i.e. The goal is to determine values of m and c which minimize the differences (residuals) between the observed values (i.e. If you have made it past algebra in school you have most likely encountered this model. what you control, such as, dose, concentration, etc.) To start let’s look at the simplest model, known as a linear regression: Let’s look at a simple model to discuss how to “fit” a curve and a more complex, “biologically relevant” model to start applying what we know.Ĭhoosing a model really depends on a thorough understanding of what it is that you are measuring (see How to Choose and Optimize ELISA Reagents and Procedures). Once the standard curve is generated it is relatively easy to see where on the curve your sample lies and interpolate a value. You can think of the standard curve as the ideal data for your assay. This set of data for the standards allows one to “fit” a statistical model and generate a predicted standard curve. The standards, in your assay, should be tested at a range of concentrations that yields results from essentially undetectable to maximum signal. ![]() ![]() In order to determine a quantity of something you will need to compare your sample results to those of a set of standards of known quantities. This is where things can get interesting. You’ll probably want to also determine the quantity of the material you have detected. Maybe you will even develop your own assay. All you have to do is test the sample using any number of commercially available kits. You have been asked to perform an ELISA to detect a molecule in a biologic matrix. ![]()
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