It is essential that evaluators do not only see these benefits in terms of number of publications produced, but also through the prism of publication quality (citations and outcomes generated) as well as individual and organizational attributes. The results of our analysis should motivate program evaluators who assess the benefits of public funding and intervention to support academic research. The gender of researcher does not seem to significantly influence the productivity variables. Generally speaking, young researchers, as well as those affiliated with Anglophone or/and large universities tend to produce more publications. We revealed that age and language (Francophone versus Anglophone) of university instruction, and, in addition, the origin of funding do affect researcher productivity.

Our findings imply that the volume of funding is not as influential as supposed. Factor analysis and linear regression served as tools for evaluation. Individual publications in referred journals (number of publications, fractioned publications, citations, impacts) were used as indicators for research productivity. We analyzed data on 194 researchers for the period of 2001-2008. The main hypothesis is that productivity in scientific research is significantly influenced by the volume and origin of the funding sources mobilized to support scientific research performance. This paper seeks to identify the factors that explain research productivity in higher education, using as a case study, the universities in Quebec-Canada. Despite the abundance of empirical works on the question of researcher productivity, there is a paucity of studies dealing with this issue in the context of higher eductaion. The relationship between research inputs (grants, infrastructure spending, training of researchers) and research outputs (number of publications, citation, impact) emerges, therefore, as a strategic issue for public decision-making on funding in support of innovation and the development of competencies. Universities play an increasingly significant role in producing new knowledge. These findings could be used to promote ecological functions in the Yangtze Estuary and other coastal marshes in the world. Thus, we proposed some salt marsh restoration strategies, such as preferential restoration of long transects, differentiation of vegetation configuration, and engineering protections like low levees or breakwaters in front of salt marsh edges. Wave attenuation along transects was also affected by both land cover and spatial configuration. Wave attenuation by salt marshes differ in various environments and are species specific. Results showed that 1) the two 10 m-wide stripes of typical vegetation (Spartina alterniflora and Scirpus mariqueter) could attenuate wave height mostly when incident wave height was 0.22 m and 0.20 m, respectively 2) under the condition of 0.6 m initial wave height, wave attenuation rate among all transects ranged from 32.50% to 98.30% (71.30 ± 16.09% on average) and 3) Land cover-Position-Length weighted LRI was an effective indicator for the relationship between wave attenuation and landscape pattern. We explored wave attenuation by salt marshes at site and transect scale, and put forward potential approaches to improve coastal defenses. This study sets an example at the Nanhui nearshore salt marshes in the Yangtze Estuary, China, with a one-dimension wave attenuation empirical model and Landscape Resistance Index (LRI). With the increase in coastal hazards induced by global change, estuarine deltas are urgently required to enhance nature-based coastal defenses. The models used for comparison are models of the Department of Higher Mathematics and Mathematical Methods in Economy of Simon Kuznets Kharkiv National University of Economics and econometric models which were published recently by leading journals. Experimental studies for validation the proposed programs were carried out for various linear regression models. To this purpose we modified few open algorithms of the implementation of known tests on heteroskedasticity. The aim of the article is to propose a MATLAB implementation of many tests used for checking the heteroskedasticity in multifactor regression models.

One of the achievements of this paper is that real empirical data are used to test for heteroskedasticity. We gave the principles of implementing the most common tests that are used to detect heteroskedasticity in constructing linear regression models, and compared their sensitivity. This can be accompanied by both an increase and a decrease in the confidence interval.

Heteroskedasticity distorts the value of the true standard deviation of the prediction errors.

The article discusses the problem of heteroskedasticity, which can arise in the process of calculating econometric models of large dimension and ways to overcome it.

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