Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Hypothesis testing provides organizations with a structured approach to evaluate assumptions using data, reducing reliance on intuition and enhancing decision accuracy. By validating decisions with ...

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 ...

Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

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 authors find that contemporary GIS research increasingly integrates spatial statistics, modeling, and simulation to move ...

Let’s start with a definition of Applied Statistics: Applied Statistics is the root of data analysis. The practice of applied statistics involves analyzing data to help define and determine an ...

How to use statistical tools for component tolerance analysis. A look at methods such as Monte Carlo and Gaussian distribution. Simulating a dc-dc converter in LTspice to model closed-loop voltage ...