From the preceding data we have obtained population estimates for certain of the California Athabascan groups. If these estimates are judged reliable, it would be desirable to use them as a basis for estimating the population of the remaining groups. When a detailed analysis of the ecological or demographical factors involved is lacking, it is sometimes necessary to fall back on rather simplistic assumptions to attain the desired end. Cook goes rather far in this direction, using simply the average population density per square mile of the known groups to estimate the population of the unknown groups. It appears to this writer that a somewhat more satisfactory method of estimation would be based on simple linear regression theory. It is a fact that pertinent relationships in population studies can often be expressed in terms of simple exponential functions or in linear combinations of logarithms. Thus we might propose a relationship such as the following: population = a + b (ln area) or population = a + b (ln fishing miles) where a and b are constants to be determined and ln is the logarithm to the base e. Of course we would not expect these relationships to be precise. The lack of exactness might be due to the crudeness of the various measurements involved or perhaps to the fact that population depends on more than one such factor. To account in some way for the uncertainty, we might make a further assumption and propose the following relationships: population = a + b (ln area) + X population = a + b (ln fishing miles) + X where X has a normal probability distribution with mean = 0 and some unknown variance = =s=^{2}. X is then, roughly speaking, the error involved in each observation. That the error would be distributed normally is quite reasonable under the circumstances. In situations where the uncertainty of the observation is due to measurement error or to a multiplicity of factors, the distribution obtained often assumes a normal form or a form sufficiently normal so that the normal distribution can be used as an approximation.