Theory of the Combination of Observations Least Subject to Errors

part one; part or mean value of the error; The mean square error as a measure of uncertainty; Mean error, weight and precision; Effect of removing the constant part; Interpercentile ranges and probable error; properties of the uniform, triangular, and normal distribution; Inequalities relating the mean error and interpercentile ranges; The fourth moments of the uniform, triangular, and normal distributions; The distribution of a function of several errors; The mean value of a function of several errors; Some special cases; Convergence of the estimate of the mean error; the mean error of the estimate itself; the mean error of the estimate for the mean value; Combining errors with different weights; Overdetermined systems of equations; the problem of obtaining the unknowns as combinations of observations; the principle of least squares; The mean error of a function of quantities with errors; The regression model; The best combination for estimating the first unknown; The weight of the estimate; estimates of the remaining unknowns and their weights; justification of the principle of least squares; The case of a single unknown; the arithmetic mean. Pars Posterior/part two; part two; Part I; Part II.