Power and Sample Size Calculations
There are no generally agreed methods for relating the number of observations versus the number of independent variables in the model. One rule of thumb suggested by Good and Hardin is, where is the sample size, is the number of independent variables and is the number of observations needed to reach the desired precision if the model had only one independent variable. For example, a researcher is building a linear regression model using a dataset that contains 1000 patients . If he decides that five observations are needed to precisely define a straight line, then the maximum number of independent variables his model can support is 4, because
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