Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.
An interdisciplinary team of University of Tennessee, Knoxville researchers recently published in Biophysical Journal on ...
This asynchronous course addresses the basic theory behind Statistical Process Control (SPC), a method used in monitoring and controlling the quality of a process through statistical analysis to ...
Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...
The Monte Carlo simulation estimates the probability of different outcomes in a process that cannot easily be predicted because of the potential for random variables.
Background The way that data are presented can influence quality and safety initiatives. Time-series charts highlight changes but do not clarify whether data lie outside expected variation.
A new ranking methodology places Barry Bonds over Babe Ruth as the game’s best player ever. Statisticians, at least, are cheering. By Alexander Nazaryan Every sport has its arguments over which player ...
This book, “Statistical Modeling and Computation,” provides a unique introduction to modern statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of ...
Abstract: Emerging transistors lack the statistical compact models needed to evaluate the yield of integrated circuits. To address this, we propose a novel framework combining a variational ...
Looking to get into statistical programming but lack industry experience? We spoke with several statistical programmers from diverse backgrounds, and one thing became clear—there’s no single path to ...
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