Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

This paper attempts to evaluate the efficiency of Indian banks using data envelopment analysis (DEA) models. The main focus of this study is to assess whether the management has any significant impact during the efficiency evaluation. The DEA used to examine the technical efficiency of public, private, and foreign sector banks under homogenous and heterogeneous environments to check whether the working environment plays a vital role in efficiency evaluation. The results have shown public sector banks are affecting more due to ownership. The public and private sector banks have shown significant improvement in their efficiency scores when evaluating under a heterogeneous environment. The foreign sector is free of ownership impact due to their small scale banking business in India. © 2020, Research Publication. All rights reserved.

World University root node

Department of Mathematics

School of Advanced Sciences

Vellore Campus

Advances in Mathematics: Scientific Journal

Assessing the environmental efficiency of indian commercial banks-an application of data envelopment analysis

In this paper we discussed the basic statistical analysis of the biological data and associated with the demographic profile and biochemical profile of the samples of arthritis. Arthritis is a multi-factorial disorder consisting of more than 100 types of arthritis9, it is really difficult to say that a particular disease is causing that disorder. For determination of Rheumatoid Arthritis (RA) we have used biochemical tests of the 70 individual affected with rheumatoid arthritis. The peripheral blood serum and plasma has been collected using vacutainers containing EDTA, the samples have gone through 5 tests for the determining the pattern of arthritis. By using this method pre-diagnostic measure for arthritis, results have shown us that most of the patient have correlative increase and decrease in their serum and plasma level when the patient is affected with RA. Various test such as Hb, Uric acid, serum glucose, serum calcium levels are used to determine the presence of arthritic profile. The results have found completely based on correlation of the demographic data with the arthritic marker, diabetic marker, lipid marker, kidney function marker, liver marker. The results have found that maximum correlation is shown among the liver marker as its maximum correlation with all the arthritic markers and demographic data. © 2020, Institute of Medico-Legal Publications. All rights reserved.

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Indian Journal of Forensic Medicine and Toxicology

Statistical analysis of rheumatoid arthritis in association with demography and biochemical investigation

In this paper we discusses on fundamental of bioassay and Fiellers theorem which is an alternative method to calculate the confidence interval of a ratio. Most of the Statistical methods deals with sums and differences of the parameters, variables but not limited to ratios, percentages. We discussed on the structure and types of Bioassays and theoretical approach of Fiellers theorem. It is used to construct the confidence intervals, providing alternatives to the exact asymmetric confidence intervals. Also discusses on two analogous of Fiellers theorem. It is a fundamental formula of statistics in bioassay. Relative potency is a term used in bioassay to refer to the ability of a test sample, of unknown potency, to produce the desired response compared to a reference sample, when tested under the same conditions. © 2020, Research Publication. All rights reserved.

Some aspects of bioassays and fiellers theorem

This study was carried out among the students born and brought in the state of Tamilnadu to determine the degree of correlation between specific cranial measurements and stature. The standing height, maximum length,breadth & circumference of the head of the students comprising 100 males and 100 females aged 18 to 25 were measured and analyzed. Mean stature of male is 170.63 cm and female is 155.48 cm. A fair sexual dimorphism was found among stature, head length, head breadth and head circumference. Highest correlation (38% at P value 0.0001) was found between stature and head length (HL) among males, and highest correlation (42% at P value 0.000) was found between stature and head circumference (HC) among females.The study demonstrated that the use of cephalic measurements for the estimation of stature is valid and worth pursuing. © 2018 South India Medico-Legal Association. All rights reserved.

Journal of South India Medicolegal Association

Estimation of stature from cephalometric measurements among students of allied medical sciences of Chennai

In this study we carried out estimation of stature on specific facial measurements among the students born and brought in the state of Tamilnadu, further we attemted to determine the degree of correlation between the facial measurements and stature. The standing height, morphological facial length, vertical length of nose, external biocular breadth, bizygomatic breadth, and bigonial breadth of the students comprising 100 males and 100 females aged 18 to 25 were measured and analysed. Mean stature of male was 170.63 cm and female was 155.48 cm. A fair sexual dimorphism was found among stature, and all facial measurements except vertical length of nose. Highest correlation was found between stature and morphological facial length, the values were among female 38.8 %, (at P value 0.0001), and male 31.9% (at P value 0.0012). The predicting power of multiple facial measurements was 22.38% and 14.83% among female and male population respectively. The study demonstrated that the use of facial measurements for the estimation of stature is valid and worth pursuing. © 2018, Indian Journal of Forensic Medicine and Toxicology. All rights reserved.

Prediction of stature on facial anthropometry among students of nursing and physiotherapy in Chennai

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested. © Published under licence by IOP Publishing Ltd.

IOP Conference Series: Materials Science and Engineering

MANCOVA for one way classification with homogeneity of regression coefficient vectors

Time series is an important part of decision making and several results are constructed on estimates of forthcoming events. In the Time series process, since, for the coming actions include probability; the predictions are frequently not perfect. The objectives of the Time series are to decrease the prediction error; to create predictions that are rarely improper and that have minor prediction mistakes. In selecting a suitable Time series model, the researcher wants to be responsive that numerous altered models may have comparable properties. A good model will fit the data well. The goodness of fit recovers as additional limits are involved in the model. This arises a problem with the ARMA (p, q) models, because p and q take low values. It should be noted that the best model fit need not imply to provide best forecasts. There exists several model selection criteria that trade off a decrease in the sum of squares of the errors for a more sparing model. A goodness of fit criterion for ARMA (p, q) model and modified selection criteria for Time series models have been suggested in the present study. © Published under licence by IOP Publishing Ltd.