Development in Statistics, Volume 1 is a collection of papers that deals with theory and application of parameter estimation in stochastic differential systems, the comparative aspects of the study of ordinary time series, and real multivariate distributions. Some papers discuss covariance analysis of nonstationary time series, nonparametric repeated significance tests, as well as discrete optimal factorial designs for statisticians and investigators of experiments. One paper cites an application of parameter estimation in stochastic differential systems in approximates of stability and control derivatives from flight test data. Another paper cites cases where procedures of ordinary time series (or point processes) have direct analogs in the study of point processes (or ordinary time series). One paper explains the applications of multivariate distributions in simultaneous tests on the equality of eigenvalues toward the covariance matrix, canonical correlation matrix, and a matrix associated with the multivariate analysis of variance. Another paper reviews two types of repeated significance tests, namely, the genuinely distribution-free tests based on a broad class of nonparametric statistics; and the asymptotically distribution-free tests based on a broad class of parametric statistics but having asymptotically nonparametric behavior. Both types can provide a unified solution to a broad class of problems. The collection can be valuable for mathematicians, students, and professors of calculus, statistics, or advanced mathematics.