This paper maps device-level statistics into subsystem-level statistics, developing an all-analytic stochastic model enabling deeper intuition into the propagation mechanisms of the mismatch impairment via the eDAC and the subsequent modules in the end-to-end transmission chain comprising the eDAC, the (optical) Tx, a linear channel and the Rx. Particular attention is given to formally proving and exploring the symmetries of the various eDAC random variates and performance metrics. As in most areas of physics, it is beneficial to reveal and explore the symmetries hidden in the structures and interactions. Moreover, the normalization of various physical quantities by judicious selection of suitable units of measurement, is an investment worth making upfront, enabling to simplify and streamline physical models. For current-steering eDACs, it is highly beneficial to normalize all currents by the so-called unary current and to explore the symmetries impliedby the block diagrammatic structure of the eDACs and the mathematical nature of the eDAC codes. In the context of optical communication, to be addressed in the upcoming Part II of this paper, the eDAC output mismatch will be tracked across the electrical-to-optical interface in the Tx, accounting for the MZM nonlinearity, all the way to the input of the decision circuit in the optical Rx, enabling to relate useful communication performance parameters such as received constellation EVM and SER at the optical Rx, to the fundamental current mismatch impairment of the eDAC drivers in the optical Tx. Our novel all-analytic behavioral statistical modeling toolset may turn useful in the early stage of eDAC and/or wireless \(\mathrm{|}\) optical Tx design cycle, to enable a grasp on the fundamentals of the design, providing insight into main features of the solution, as useful addition to Monte Carlo simulations, e.g., [10; 11; 12] which are useful but are best deferred to fine-tuning and final verification in the later stages of eDAC + Tx design cycle, once all-analytic pre-design (enabled by our new toolset) has been completed. Our analytical stochastic analysis framework pertains to performance metrics such as EVM, SER and BER, important to any communication engineer, regardless of the physical nature of the transmission channel (EVM is directly addressed in this paper, SER/BER are addressed in Part II). Since the electrical DAC is an essential building block in any type of digital communication system at large, it follows that most of this paper may be found useful for readers with no interest in optical communication, but rather oriented to wireless or wireline transmission (e.g., to model EVM \(\mathrm{|}\) SER \(\mathrm{|}\) BER of wireless links, quantifying the impairments due to electronic mismatch propagation via the nonlinearities of power amplifiers). To preview some of the highlights of the paper by way of graphic visualization (which we suggest skimming ahead of delving into the gradual mathematical modeling construction), the matrix models we develop for the eDACs are mapped in Figures 1 and 2 into behavioral block diagrams for generation of Unipolar (UNIP) and Bipolar (BIP) eDACs of orders 4, 8 and 16 (e.g., a UNIP ePAM4 constellation is of the form {0, \(\mathrm{I_{u}}\), \(\mathrm{2I_{u}}\), \(\mathrm{3I_{u}}\)}, all non-negative, whereas a BIP ePAM4 constellation is of the form {\(-\mathrm{3I_{u}}\), \(-\mathrm{I_{u}}\), \(\mathrm{I_{u}}\), \(\mathrm{3I_{u}}\)}/2\), containing \(\pm\) levels). This paper is concerned with the statistics of the mismatch-induced variations in the eDAC output levels. The statistics turns Gaussian to an excellent approximation, determined solely by the means and variances. Figures 3 and 4 are also worth previewing as they illustrate some striking statistical properties of the output constellations, the statistics of which is derived from the statistics of the independent current sources.