ABSTRACT
The least squares method produces an estimated line which is the best fitting of all possible straight lines, at least in the sense of minimizing the sum of squared residuals. If a random variable has a normal distribution, with a specified mean and variance, it is possible to find the probability that the variable will take a value within any chosen interval. One of the contexts in which it is important to be able to recognize linearity, for if two unknown quantities have to satisfy two linear equations, the only possible solution is a pair of values which define a point lying on both straight lines. Since two distinct straight lines can only cross at a single point, there can only be one value, for each unknown, which satisfies both equations.
