Editing Regression analysis (section)

==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 method conjectured by Good and Hardin is <math>N=m^n</math>, where <math>N</math> is the sample size, <math>n</math> is the number of independent variables and <math>m</math> is the number of observations needed to reach the desired precision if the model had only one independent variable.<ref>{{cite book |last1=Good |first1=P. I. |author1-link=Phillip Good|last2=Hardin |first2=J. W. |title=Common Errors in Statistics (And How to Avoid Them)|publisher=Wiley|edition=3rd|location=Hoboken, New Jersey|year=2009|page=211|isbn=978-0-470-45798-6}}</ref> For example, a researcher is building a linear regression model using a dataset that contains 1000 patients (<math>N</math>). If the researcher decides that five observations are needed to precisely define a straight line (<math>m</math>), then the maximum number of independent variables (<math>n</math>) the model can support is 4, because

: <math>\frac{\log 1000}{\log5}\approx4.29 </math>.