A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time.

Commentary on Olvera et al.’s Article Entitled, “Physical Activity in Latino Children: Research and Its Implications”

The following is a commentary on an article discussing physical activity in Latino children. It is clear that research is needed to determine the causes of inactivity and develop effective strategies for promoting physical activity in this population. Approaches involving numerous community entities (faith-based, businesses) and the implementation of policies that enhance physical activity participation appear very promising.

Comment on “The phase relation between atmospheric carbon dioxide and global temperature” Humlum et al. [Glob. Planet. Change 100: 51–69.]: Isotopes ignored

A recent study relying purely on statistical analysis of relatively short time series suggested substantial re-thinking of the traditional view about causality explaining the detected rising trend of atmospheric CO2 (atmCO2) concentrations. If these results are well-justified then they should surely compel a fundamental scientific shift in paradigms regarding both atmospheric greenhouse warming mechanism and global carbon cycle. However, the presented work suffers from serious logical deficiencies such as, 1) what could be the sink for fossil fuel CO2 emissions, if neither the atmosphere nor the ocean – as suggested by the authors – plays a role? 2) What is the alternative explanation for ocean acidification if the ocean is a net source of CO2 to the atmosphere? Probably the most provocative point of the commented study is that anthropogenic emissions have little influence on atmCO2 concentrations. The authors have obviously ignored the reconstructed and directly measured carbon isotopic trends of atmCO2 (both δ13C, and radiocarbon dilution) and the declining O2/N2 ratio, although these parameters provide solid evidence that fossil fuel combustion is the major source of atmCO2 increase throughout the Industrial Era.