We establish a rigorous procedure for testing a general hypothesis on an arbitrary subset of regression coefficient functions. Specifically, we exploit the techniques developed for post-regularization ...

A t-test is an inferential statistic used in hypothesis testing to determine if there is a statistically significant difference between the means of two samples.

For multiple hypothesis testing in genomics and other large-scale data analyses, the independent hypothesis weighting (IHW) approach uses data-driven P-value weight assignment to improve power ...

We describe a novel spatially and temporally detailed approach for determining the cause or causes of a population decline, using the western Alaskan population of Steller sea lions (Eumetopias ...

Linear regression was easy, right? Now, let's check out t-test analysis using R.

Hypothesis testing is a procedure for evaluating the strength of a hypothesis. The methodology depends on the data and the reason for the analysis.

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

In this work, we propose a robust multiple-locus regression approach for analyzing multiple quantitative traits without normality assumption.

This article explores the concept of statistical hypothesis testing, its process, and how organizations can leverage it to make better business decisions. We’ll also examine real-world examples ...