The bunching technique, a standard procedure in Feynman-α analysis, introduces strong correlations between the data points of the resulting variance-to-mean (Y[jls-end-space/]) curve. This necessitates the use of a full covariance matrix for robust parameter fitting. This paper provides a fundamental, analytical investigation into the origin and mathematical structure of these bunching-induced correlations.To isolate the correlations generated by the bunching process itself from those of the underlying physics, we introduce a simplified toy model. This model assumes the initial sequence of binned neutron counts to be independent and identically distributed (i.i.d.) Gaussian random variables. We then derive analytical expressions for the three fundamental covariance components that constitute the Y value’s covariance matrix: (i) the covariance between bunching means, (ii) the covariance between a bunching mean and a bunching variance, and (iii) the covariance between two bunching variances.Our derivation, combining the generalized Isserlis’ theorem with complex analysis and linear Diophantine equations, yields exact closed-form expressions for these components. The final covariance matrix of the Y curve is then constructed via Gaussian error propagation. The results demonstrate that the characteristic structures of the correlation matrix observed in experiments and full simulations are primarily a mathematical artifact of the bunching algorithm itself and are largely independent of the underlying physical process. This work provides a fundamental theoretical framework for understanding data correlations in reactor noise analysis.