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• Open Access   Article

  Modification of Third-Order Iterative Method without Second Derivative for Solving Nonlinear Equation

  Wartono, M. Soleh, R. K. Pertiwi

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.1-7, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.17

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  Abstract
  In this paper, we propose a new two-point iterative method with two real parameters for solving nonlinear equation. We derive the method based on the linear combination of two third-order iterative methods. The proposed method still contains first and second derivative of f(x). To reduce the number of functional evaluations, the first and second derivative are approached by equality of two third-order iterative methods and parabolic equation, respectively. The convergence analysis shows that the proposed method has fourth-order convergence for  = ( 2)/(  0.5). The proposed method requires three evaluation of functions per iteration with efficiency index equal to 1.5874. Numerical simulation is presented to illustrate the comparison of efficiency and performance of the proposed method, Newton’s method, Chebyshev’s method and Homeier’s method by using several test functions. The result of numerical simulation shows that the performance of the proposed method better than other discussed methods.

  Key-Words / Index Term
  Efficiency index, Chebyshev’s method, Homier’s method, order of convergence, nonlinear equation

  References
  [1] S. Amat, S. Busquier, and J. M. Gutierrez, “Geometric construction of iterative funcion to solve nonlinear equation”, Journal of Computational and Applied Mathematics, vol. 197, pp. 654658, 2008.
  [2] C. Chun, “A one-parameter family of third-order methods to solve nonlinear equations,” Applied Mathematics and Computation, vol.189, pp. 126130, 2007.
  [3] J. R. Sharma, “A family of third-order methods to solve nonlinear equations by quadratic curves approximation,” Applied Mathematics and Computation, vol. 190, pp. 5762, 2007.
  [4] S. Amat, S. Busquier, J. M. Gutierrez, and M. A. Hernandez, “On the global convergence of Chebyshev’s iterative method”, Journal of Computational and Applied Mathematics, vol. 220, pp. 17 – 21, 2008.
  [5] D. Jiang and D. Han, “Some one-parameter families of third-order methods for solving nonlinear equations”, Applied Mathematics and Computational, vol. 195, pp. 392 – 396, 2008.
  [6] C. Chun and Y. Kim, ”Several new third-order iterative methods for solving nonlinear equations”, Acta Applicandae Mathematicae, vol. 109(3), pp. 1053 – 1063, 2010.
  [7] S. Abbasbandy, “Improving Newton-Raphson method for nonlinear equation by modified Adomian decomposition method”, Applied Mathematics ans Computation, vol. 145, pp. 887 – 893, 2003.
  [8] C. Chun, “Iterative method improving Newton’s method by the decomposition method”, Computers and Mathematics with Applications, vol. 50, pp. 1559 – 1568, 2005.
  [9] M. Javidi, “Iterative methods to nonlinear equations”, Applied Mathematics and Computation, vol. 193, pp. 360 – 365, 2007.
  [10] M. A. Noor, “Iterative methods for nonlinear equations using homotopy pertubation method”, Applied Mathematics and Informations Sciences, vol 4(2), pp. 227 – 235, 2010.
  [11] A. Rafiq and A. Javeria, “New iterative method for solving nonlinear equation by using modified homotopy pertubation method”, Acta Universitatis Epulensis, vol. 8, pp. 129 – 137, 2009.
  [12] S. Abbasbandy, “Modified homotopy perturbation method for nonlinear equations and comparison with Adomian decomposition method”, Applied Mathematics and Computation, vol. 172, pp. 431 – 438, 2006.
  [13] F. A. Shah and M. A. Noor, “Variational iteration technique and some methods for the approximate solution fo nonlinear equations”, Applied Mathematics and Infomations Sciences Letters, vol. 2(3), pp.85 – 93, 2014.
  [14] S. Weerakoon and T. G. I. Fernando, “A Variant of Newton’s Method with Accelerated Third-Order Convergence,” Applied Mathematics Letters, vol. 13, pp. 87 – 93, 2000.
  [15] A. Y. Ozban, “Some new variant of Newton’s method”, Applied Mathematics Letters, vol. 17, pp. 677 – 682, 2004.
  [16] H.H.H. Homeier, “ On Newton-Type Methods With Cubic Convergences,” Applied Mathematics adn Computation, vol. 176, pp. 425 – 435, 2005.
  [17] O. Y. Ababneh, “New Newton’s method with third-order convergence for solving nonlinear equations”, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, vol 6(1), pp. 118 – 120, 2012.
  [18] K. Jiseng, L. Yitian, and W. Xiuhua, “A Composite Fourth Order Iterative Method For Solving Non-linear Equation,” Applied Mathematics and Computation, vol. 184, pp. 471–475, 2007.
  [19] C. Chun and Y. M. Ham, “Some fourth-order modifications of Newton’s method”, Applied Mathematics ans Computaion, vol. 197, pp. 654 – 658, 2008.
  [20] Z. Xiaojian, “Modified Chebyshev-Halley’s methods free second derivative”, Applied Mathematics and Computation, vol. 203, pp. 824 – 827, 2008.
  [21] J. F. Traub, “Iterative Methods for the Solution of Equation,” Prentice Hall,Inc., Englewood, 1964.
  [22] F.A. Potra, V. Pták, Nondiscrete induction and iterative processes, Research Notes in Mathematics, vol. 103, Pitman, Boston, 1984
  [23] J. R. Sharma, A composite third order Newton–Steffensen method for solving nonlinear equations, vol. 169, pp. 242 – 246, 2005.
  [24] A.M. Ostrowski, Solutions of Equations and System of Equations, Academic Press, New York, 1960
  

  Citation
  Wartono, M. Soleh, R. K. Pertiwi, "Modification of Third-Order Iterative Method without Second Derivative for Solving Nonlinear Equation," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.1-7, 2019

• Open Access   Article

  Statistical Analysis of Rainfall Moving Trend in Telangana State

  B.Vittal, M. Krishna Reddy

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.8-15, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.815

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  Abstract
  Changes of Climate is natural phenomenon, but high or unexpected changes are not expected to be uniform, rather it may be differ from one demographic region to other. The study was conducted to establish the rainfall trends in Telangana 10 Districts. To analyze the significance of rainfall t-test is used for test the data and changes between three seasonal rainfalls ANOVA has been used for analysis. The difference between pairs of the variables (Seasons) HSD Post-Hock test is used for analysis under study. From the analysis, significant differences were not being observed among the zones but there is a difference in between the seasons. Within the zone the rainfall play dominant role but not between the zones. However the rainfalls within aforementioned zones were oscillatory status.

  Key-Words / Index Term
  Rainfall, Trend, Linear regression, t-test, ANOVA, Tukey’s post-Hock test

  References
  [1] R. Khavse*, R. Deshmukh, N. Manikandan, J. L Chaudhary and D. Kaushik “ Statistical Analysis of Temperature and Rainfall Trend in Raipur District of Chhattisgarh”, Vol. 10(1), 305-312, 2015.
  [2] Manickam Valli*, Kotapati Shanti Sree and Iyyanki V Murali Krishna “ Analysis of Precipitation Concentration Index and Rainfall Prediction in various Agro-Climatic Zones of Andhra Pradesh, India”, Vol. 2(5), 53-61, 2013.
  [3] Theresa Hoang Diem Ngo, Warner Bros. Burbank, CA “The Box-Jenkins Methodology for Time Series Models”, Paper 454, 2013.
  [4] Amaha Gerretsadikan and M.K.Sharma “Modeling and forecasting of rainfall data of mekele for Tigray region (Ethiopia)”, Volume 9, Nos. 1&2, 2011.
  [5] Fulekar, M.H. and Kale, R.K., “Impact of Climate Change: Indian Scenario,” University News, 48(24): 14-20; 15-23, 2010.
  [6] S. Soltani, R. Modarres and S.S. Eslamian, “ The use of timeseries modeling for the determination of rainfall climates of Iran”. International Journal of Climatology, 27: 819-829, 2007.
  [7] Bowerman, Bruce L., Richard T. O’Connell, and Anne B. Koehler. “Forecasting, Time Series, and Regression”, 4th ed. Belmont, CA: Thomson Brooks/Cole, 2005.
  [8] G.E.P Box, G.M Jenkins, “Time Series Analysis, Forecasting and Control”, Holden-Day: San Francisco, 1976.
  

  Citation
  B.Vittal, M. Krishna Reddy, "Statistical Analysis of Rainfall Moving Trend in Telangana State," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.8-15, 2019

• Open Access   Article

  Modeling and Monitoring Cereals Crop Production Based On Parametric and Nonparametric Regression Models

  S.S. Suba, A. Rajarathinam

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.16-29, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.1629

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  The present investigation is carried out to study the trends in cereals crops production in India for the period 1960-1961 to 2016-2017 based on the parametric and nonparametric regression models. In parametric models different linear models are employed. The statistically most suited parametric models are selected on the basis of highest adjusted R2, significant regression co-efficient and co-efficient of determination (R2). Appropriate model is selected based on the model performance measures such as, Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, assumptions of normality and independence of residuals. Nonparametric estimates of underlying growth functions are computed at each and every time points. Relative growth rates of crop productions are estimated based on the best fitted trend functions. In this study none of the parametric models are found suitable to study the trend. Nonparametric regression with independent error model is found suitable to study the trends. An increase in trend has been observed in the cereals crop production and productivity in spite of decrease in area under the crop. The average percent growth rate values obtained for the successive years during the study period for the area, production and productivity when averaged shows that the production has increased at a rate of 1.03 per cent per annum due improvement in yield (2.09 per cent per annum) even though the area decreased at a rate of 1.08 per cent per annum.

  Key-Words / Index Term
  Adjusted R2, Shapiro –Wilk test, Run test, Kernal Smoothing, Nonparametric Regression

  References
  [1] A.K Dey, “Rate of growth of agriculture and industry, Econ.Political Weekly”, pp.A26-A30, 1975.
  [2] W. Hardle, “Applied Non-parametric Regression”, Cambridge Univ. Press, 1990.
  [3] P.K Joshi, R.A.Saxena, “Profile of pulses production in India: Facts, trends and Opportunities”, Ind. J. Agric. Econ, Vol. 57, pp.326-339, 2002.
  [4] D.C. Montgomery, E.A. Peck, and G.G.Vining, “Introduction to Linear Regression Analysis”, John Wiley & sons, Inc, 2003.
  [5] P.Narain, R.K. Pandey, and S.Sarup, “Perspective plan for food grains. Commerce”,
  Vol. 145, Issue, 14,pp. 184-191,1982.
  [6] S.K.Nath, “Manual on area and crop production Statistics”, Central Statistical Organisation, 2008.
  [7] V.G.Panse, “Yield trends of rice and wheat in first two five-year plans in India”, J. Ind. Soc.Agril. Statist, Vol. 16, pp 1–50, 1964.
  [8] B. N.Patil, S.R.Bhonde, and D.N.Khandikar, “Trends in area, production and productivity of groundnut in Maharashtra”, Financing Agriculture, pp.35-39, 2009.
  [9] N Reddy, “Growth rates. Economic and Political weekly”, Vol. 13, pp 806–812, 1978.
  [10] A.Rajarathinam, R.Vetriselvi, D.Balamurugan, “Unobserved Components model for forecasting Wheat Production”, Int.J.Agrcult.Stat.Sci, Vol.12, No.1, pp.161-167, 2016.
  [11] A.Rajarathinam, R.Vetriselvi, “Modeling Cereals Crop Production Based on Parametric and Nonparametric Models”, Int.J.Agrcult.Stat.Sci, Vol. 13, No.1, pp.17-28, 2017.
  [12] A.Rajarathinam, R.Vetriselvi, “Modeling and Monitoring of paddy production in Southern Districts of Tamil Nadu”, Int.J.Agrcult.Stat.Sci, Vol.13, No.1, pp.181-190, 2017.
  [13] M.Sarma, “Study on agricultural growth performance of Assam. Economic. Affairs”, Vol.50, pp.38-41, 2005.
  [14] A.N.Shah, H .Shah, and N .Akmal, “Sunflower area and production variability in Pakistan: Opportunities and constraints”, HELIA, Vol.28, pp.165-178, 2005.
  [15] A. Singh, R.S.L. Srivastava, “Growth and instability of sugarcane production in UttarPradesh: A regional study”, Ind. J. Agri. Econ, Vol.58, pp. 279-282, 2003.
  

  Citation
  S.S. Suba, A. Rajarathinam, "Modeling and Monitoring Cereals Crop Production Based On Parametric and Nonparametric Regression Models," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.16-29, 2019

• Open Access   Article

  An Alternative Method for Optimal Cost with Double Acceptance Sampling Plan

  S. Kumar

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.30-39, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.3039

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  In this study, a double acceptance sampling plan is developed for cost optimization with the constraints. The good and bad item scheme is mainly considered, which totally depends on the sampling plan. The purpose of this paper is to design an economic acceptance sampling plan to obtain optimal total cost on the basis of average total inspection (ATI). In double acceptance sampling plan, we overcome the drawback of single acceptance sampling plan, when we fail to make a decision on the basis of SASP. The total cost is analyzed according to various parameters like OC, AOQ, ASN and ATI. A mathematical model has been developed to find minimum total cost with consideration of various cost (inspection cost, failure cost and post-sale failure cost) and provides a protection both the producer’s and consumer’s risk. The results are explained with different tables and graphs.

  Key-Words / Index Term
  Acceptance Sampling Plan, Average Total Inspection, Total Cost, Average Outgoing Quality, Average Sample Number, Operating Characteristic

  References
  [1]Dodge, H.F. and Romig, H.G. (1959): ‘‘Sampling inspection tables, Single and Double sampling’’, 2nd edition, John Wiley, New York.
  [2] Duncan, A. J. (1963): “Quality Control and Industrial Statistics” Irwin: Homewood, IL
  [3] Ekambaram, S. K. (1960): “The statistical basis of Quality control charts”, Asia publishing house.
  [4] Forrell, W.G. and Chhoker, A. (2002): “Design of economically optimal acceptance sampling plan with inspection error”, Computers and operations Research, 29, 10, 1283-1300.
  [5] Grant, E. L. (1964): “Statistical Quality Control”, Mc-Graw Hill, New York.
  [6] Hsu,L.F. and Tapiero,C.S.(1990): “An economic model for determining the optimal quality and process control policy in a queue like production system”, International Journal of Production Research,vol.28,no.8.pp.1447-1457.
  [7] Kapur, K.C. and Lamberson, L.R. (1977): ‘‘Reliability in Engineering Design”, John Wiley, New York.
  [8] Kapur, K.C. and Wang, C.J. (1987): “Economic Design of Specifications Based on Taguchi’s Concept of Quality Loss Function”, Quality Design Planning and Control, edited by DeVor, R.E. and Kapoor, S. G. The Winter Annual Meeting of The American Society of Mechanical Engineers, Boston, MA, U.S.A. pp. 23-36.
  [9] Kapur, K.C.(1988): “An approach for development of specifications for quality improvement”, Quality Engineering, 1, 63–77.
  [10] Hsu, L. F. and Hsu, J. T. (2012): “Economic Design of Acceptance Sampling Plans in a Two-Stage Supply Chain”, Advances in Decision Sciences, vol. 28, no. 8,
  [11] Montgomery, D. C. (2008): “Introduction to Statistical Quality Control”, 6th ed. Wiley.
  [12] Rahim, M.A. and Tuffaha, F.(2004): “Integrated model for determining the optimal initial settings of the process mean and the optimal production run assuming quadratic loss functions”,
  International Journal of Production Research, 42 (16), 3281-3300.
  [13] Kumar, S. (2018). “Cost Optimization Using Acceptance Sampling Plan: A Statistical Analysis with Single Sample.” Int. J. Curr. Microbiol. App. Sci. 7(02), 3759-3768.
  [14] Kumar, S. (2019). “An Alternative Method for Outgoing Quality with Double Sampling Plan.” Int. J. Curr. Microbiol. App. Sci. 8(05), 98-108.
  [15] Shewhart, W.A. (1931) “Economic control of quality of manufactured products”, Van Nostrand.
  [16] Tagaras, G. (1994): “Economic acceptance sampling plans by variables with quadratic quality cost”, IIE Transactions, 26, 6, 29-36.
  [17] Taguchi, G. (1986): Introduction to Quality Engineering, Asian Productivity Organization, Tokyo.
  

  Citation
  S. Kumar, "An Alternative Method for Optimal Cost with Double Acceptance Sampling Plan," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.30-39, 2019

• Open Access   Article

  Some Problems of Non Homogeneous Differential Equation of First Order and First Degree, Solved By the Method of Homogeneous Differential Equation of First Order and First Degree

  Arun Joshi, Anuj Joshi, Ritu

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.40-43, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.4043

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  Abstract
  This is based on the some Non homogeneous differential equation of first order and first degree which is solved by the method of homogeneous differential equation of first order and first degree, because some types of non homogeneous differential equation problems are not solved by all the method of first order and first degree except homogeneous differential equation method of first order and first degree. This method gives a perfect general solution of some non homogeneous differential equations of first order and first degree.

  Key-Words / Index Term
  Differential equation, First order, First degree, Variable seprable, Homogeneous differential equation, Non homogeneous differential equation, Linear differential equation, bernoulli difference equation, Exact differential equation

  References
  [1] Sankar Prasad Mondal , Tapan Kumar Roy, “ Application of first order non homogenous fuzzy differential equation”, Pushpa Publishing House, Allahabad, India,Volume 16, Pages 1-29, Number 1, 2013.
  [2] Djilali Behloul, Sui Sun Cheng, Computation of rational solutions for a first-order non linear differential equation”, Electronic Journal of Differential Equations, Vol. 2011 (2011), No. 121, pp. 1–16, ISSN: 1072-6691.
  [3] Sankar Prasad Mondal, Tapan Kumar Roy, “ First order linear non homogeneous ordinary differential equation in fuzzy environment based on laplace transform” , J. Math. Comput. Sci. 3 (2013), No. 6, 1533-1564, ISSN: 1927-5307, 2013
  [4] Mahtab Uddin, M. A. Ullah, “A Modified Predictor-Corrector Formula For Solving Ordinary Differential Equation Of First Order And First Degree”, IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728. Volume 5, Issue 5, PP 21-26, Mar. - Apr. 2013.
  [5] E. L. Ince, “Ordinary Differential Equations”, Dover Publications, New York, 1956
  [6] U.S.Rana, “Differential Equations”, S.Chand and Company Limited, New Delhi India, pp. 9-92,2017.
  [7] V.P.Mishra, Jyoti Sinha, pratibha Mishra, “ Advance Engineering Mathematics vol.I”, Bhavya Books , New Delhi India, pp.537-574, August 2018.
  [8]P.Sivaramakrishna Das, C.Vijayakumari, “ Engineering mathematics”, pearson, India, pp.10.6-10.56, 2018.
  

  Citation
  Arun Joshi, Anuj Joshi, Ritu, "Some Problems of Non Homogeneous Differential Equation of First Order and First Degree, Solved By the Method of Homogeneous Differential Equation of First Order and First Degree," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.40-43, 2019

• Open Access   Article

  Certain Structures of Q-Fuzzy Soft Ideals of Near-Ring

  T. Portia Samathanam, G. Subbiah, M. Navaneethakrishnan

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.44-48, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.4448

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  Abstract
  In this paper, we introduce and study Q-fuzzy soft sub near-ring and Q-fuzzy soft ideals of near-rings by Molodtsov’s definition of the soft set. Some related properties are investigated and illustrated by a great ideal of example.

  Key-Words / Index Term
  Near-ring, soft set, Q-fuzzy set, Q-fuzzy soft set, Q-fuzzy soft sub near-ring, Q-fuzzy soft ideal

  References
  [1] Dutta T.K, Suji Kumar Sardar, Sarbani “Fuzzy ideals extension in Γ-semi rings”, International Mathematical Forum,Vol.6,no.18,2011,857- 866.
  [2] Dutta and Chanda, “Structures of Fuzzy ideals of γ-ring”, Bull. Malays. Maths. Sci. Soc.(2)28(1),2005,9-18.
  [3] Hedayati.H,Shum K.P, “An Introduction to Γ-semi rings”, International Journal of Algebra, Vol.5, no.15,2011,709-726.
  [4] Jun Y.B. and Lee C.Y., “Fuzzy Γ-semi ring”, Pusan Kyongnam Math.J.8,1992,163-170.
  [5] John N Mordeson and D.S.Malik, “Fuzzy Commutative Algebra”, World Scientific Publishing Co. Pvt. Ltd.
  [6] M.K.Rao, Γ-semi ring-1, Southeast Asian Bulletin of Maths, 19,1995,49-54.
  [7] Nobusawa.N, “On generalization of the ring theory”, Osaka J.Math.1,1978,185-190.
  [8] Rajesh Kumar, “FuzzyAlgebra”, University Press, University of Delhi -110007.
  [9] V.Vanitha, G.Subbiah, M.Navaneetha Krishnan, Flexible fuzzy softification of group structures, International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.5 (4) PP 389-394.
  [10] Zadeh.L.A., “Fuzzy sets”, Information and Control 8, 1965,338-353.
  

  Citation
  T. Portia Samathanam, G. Subbiah, M. Navaneethakrishnan, "Certain Structures of Q-Fuzzy Soft Ideals of Near-Ring," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.44-48, 2019

• Open Access   Article

  Preliminary Statistical Analysis of South Central Railway Zone Income and Passengers–A Case Study

  M. Sailaja, A. Rajendra Prasad

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.49-57, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.4957

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  Abstract
  In recent times railways are playing vital role in transportation of goods and passengers throughout India. Further demand for railway zone and more number of trains is increasing day by day. The South Central Railway (SCR) is one of the 18 zones of Indian Railways which contains the states of Maharashtra, Telangana, and some portions of Madhya Pradesh. It has three divisions under its administration, which include Nanded, Secunderabad and Hyderabad. There is a necessity to analyse and forecast the facilities provided by the railway department to passengers for necessary improvements. This paper deals with the relationship between the number of passengers travelled and income generated to South Central Railway (SCR) Zone. The data was collected from January 2009 to December 2017 and analyzed using various Statistical Techniques like Curve fitting, Correlation, Regression, ANOVA. It was found that the second degree parabola is the best fit for the data and there is high degree positive correlation between number of passengers and income.

  Key-Words / Index Term
  Correlation, Regression, ANOVA, SCR

  References
  [1]. Gupta S.C. and KapoorV.K.2000.”Fundamental of Mathematical Statistics”, NewDelhi: S. Chand Company.
  [2]. Gupta S.C. and KapoorV.K.2000.”Fundamental of Applied Statistics”. Third Edition. NewDelhi:S.Chand Company.
  [3]. Parimal Mukhopadhyay “Applied Statistics” New Delhi: New Central Book Agency.
  [4]. Daroga singh & F.S. Chowdhary “Theory & Analysis of ,Sample Survey designs” New Delhi: Wiley Eastern India.
  [5]. . C.R.Rao, "LinearStatisticalInference anditsApplications"
  [6]. F.A.Graybill,"AnIntroductiontoLinearStatisticalModel"
  [7]. Glutman,"LinearModels–AnIntroduction"
  [8]. N.R. Draper and H. Smith,"Applied Regression Analysis"
  

  Citation
  M. Sailaja, A. Rajendra Prasad, "Preliminary Statistical Analysis of South Central Railway Zone Income and Passengers–A Case Study," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.49-57, 2019

• Open Access   Article

  Some Common Fixed Point Theorems in 2-Metric Spaces for Contractive Type Conditions

  Tarun Gain, Kalishankar Tiwary

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.58-61, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.5861

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  Abstract
  Our aim of this paper is to find some common fixed point theorems in 2-metric spaces by using Nesic type contractive condition, which are generalizations of various known results.

  Key-Words / Index Term
  Fixed point, 2-metric space,Contraction, Nesic type

  References
  [1]. M. E. Abd EL-Monsef A. Donia, H. M. & AbdRabou, Kh, “New types of common fixed Point theorems 2- Metric spaces”, Chaos, Soliton and Fractals, 41, 1435-1441, 2009.
  [2]. S. Gahler, “2-metrische Raume und iher topologische structur”, Math.Nachr, 26, 115-148, 1963.
  [3]. K. Iseki, P. L. Sharma, B.K. Sharma, “Contraction type mapping on 2-metric Space”, Math.Japonica, 21, 67- 70, 1976.
  [4]. M. Imdad,M. S. Kumar and M. D. Khan, “A Common fixed point theorem in 2-Metric Spaces”, Math., Japonica, 1991, 36(5), 907-914. Int J Anal Appl., 1(2):127-132, 2013.
  [5]. S. N. Lal & A. K. Singh, “An analogue of Banach`s Contraction principle for 2-metric space”, Bull. Austral. Math. Soc. Vol. 18, pp. 137-143, 1978.
  [6].D. Lateef, “Fixed point theorems in 2-metric space for some contrative conditions”, IJSIMR, Volume 6, Issue 1, PP 16-20, 2018.
  [7]. S. C. Nesic, “A theorem on contractive mapping”, Mat. Vesnik , 51-54, 1992.
  [8]. S. V. R. Naidu and J. R. Prasad, “Fixed point theorems in 2-metric spaces”, Indian J. Pure. Appl. Math, 17(8) 97-993, 1986.
  [9]. L. S. Singh and S. Singh, “Some fixed point theorems in 2-metric space”, International transactions in Mathematical Sciences and Computer. Vol-3 No. 1 , H. pp.121-129, 2010.
  

  Citation
  Tarun Gain, Kalishankar Tiwary, "Some Common Fixed Point Theorems in 2-Metric Spaces for Contractive Type Conditions," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.58-61, 2019

• Open Access   Article

  Percentage Points of Transmuted Exponentiated Gumbel Distribution (TEGD)

  Deepshikha Deka, Bhanita Das

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.62-66, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.6266

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  Abstract
  This paper deals with the percentage points of Transmuted Exponentiated Gumbel Distribution (TEGD) and its order statistics. Equations are obtained for the Percentage points of TEGD and the Percentage points of smallest and largest order statistics for a sample of size n for TEGD. Percentage points are calculated at different significant levels using R program.

  Key-Words / Index Term
  Transmuted Exponentiated Gumbel Distribution (TEGD), Percentile equation, Percentage points

  References
  [1] Akahira, M.A. (1995). A higher order approximation to a percentage point of the Non-central t-distribution. Communications in Statistics: Simulation and Computations. 24, 595-605.
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  Citation
  Deepshikha Deka, Bhanita Das, "Percentage Points of Transmuted Exponentiated Gumbel Distribution (TEGD)," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.62-66, 2019

• Open Access   Article

  Eigenvalues of Status Sum Adjacency Matrix of Graphs

  S.Y. Talwar, H.S. Ramane

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.6 , Issue.4 , pp.67-69, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i4.6769

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  Citation

  Abstract
  The distance between two vertices is the length of a shortest path joining them. The status (u) of vertex u in a connected graph G is defined as sum of distances between u and all other vertices in graph G. The status sum adjacency matrix of a graph G is SA(G) = [sij] in which sij = (u) + (v) if u and v are adjacent vertices and sij = 0, otherwise. In this paper we initiate the study of eigenvalues of status sum adjacency matrix of graphs.

  Key-Words / Index Term
  Distance in graphs, status of a vertex, status sum adjacency matrix, eigenvalues

  References
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  Citation
  S.Y. Talwar, H.S. Ramane, "Eigenvalues of Status Sum Adjacency Matrix of Graphs," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.67-69, 2019