Prism allows you to analyze linear regression from either a single or multiple datasets with shared or individual X axes. I'm James Clark from Kings College London and in this short video, I'm going to run through the steps needed to undertake linear regression analysis of a dataset in Graph Pad Prism.
This video is part of the Essential Statistics series, presented by Dr James Clark, from the School of Cardiovascular Medicine and Sciences at King’s College London.
Format and annotate graphs of your results. Navigate the results tab of the analysis. Select the appropriate analysis choices. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.This video walks you through the steps required to perform linear regression analysis of a data set in Prism. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ −0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment.
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