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Several types of research showed math anxiety as the learning outcome, but another that predictor variable. Math was predicted based on other variables, such self-regulated and self-concept. Self-regulated is associated with academic achievement. This study aimed to obtain valid significant indicators each latent variable develop structural model those variables students’ The used an interval s...

Continuous random variables are random quantities that are measured on a continuous scale. They can usually take on any value over some interval, which distinguishes them from discrete random variables, which can take on only a sequence of values, usually integers. Typically random variables that represent, for example, time or distance will be continuous rather than discrete. Just as we descri...

Self-normalized processes arise naturally in statistical applications. Being unit free, they are not affected by scale changes. Moreover, self-normalization often eliminates or weakens moment assumptions. In this paper we present several exponential and moment inequalities, particularly those related to laws of the iterated logarithm, for self-normalized random variables including martingales. ...

Knowledge of factors influencing animal abundance is important to wildlife biologists developing management plans. This is especially true for economically important species such as blackbirds (Icteridae), which cause more than $100 million in crop damages annually in the United States. Using data from the North American Breeding Bird Survey, the National Land Cover Dataset, and the National Cl...

This paper presents a multilevel quasi-Monte Carlo method for interval analysis, as computationally efficient high-dimensional linear models. Interval analysis typically requires global optimization procedure to calculate the bounds on output side of computational model. The main issue such is that it numerous full-scale model evaluations. Even when simplified approaches vertex are applied, req...