A Predictor of How Many Books a University Library will Purchase Using Regression Control

Abstract:

A library should select new books in accordance with a rational principle, which make it necessary to construct a prediction model for buying books. The paper provides a reasonable criterion to conduct university library to construct an efficient collection. The change of library lending depends on the change of the number of readers and on the change of the books that a library possesses. The author has investigated library lending and the number of readers of a university library in China for 18 school years and finds that the sample data fit the simple linear regression model. Then the prediction equation and the prediction value of library lending in the school year 2012-2013 is obtained. The author has observed library lending and the library collection for 11 school years and finds that the sample data fit the regression model.. Then Based on the control theory of the regression analysis, the control equation and the control value of the collection in the school year 2012-2013 is obtained according to the prediction value of library lending. The control value of the collection minus the observed value of the collection in last school year 2011-2012 is the library demand for new books in the school year 2012-2013.

• Periodical: Applied Mechanics and Materials (Volume 441)
• Pages: 530-533
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.441.530
• Online since: December 2013
• Authors: Bao Sun
• Keywords: Control, Library Collection, Library Demand for New Books, Library Lending, Number of Readers, Simple Linear Regression, Test

References

[1] http: /www. statsoft. com/textbook/time-series-analysis.

[2] http: /www. crowdcast. com/blog/2010/04/02/not-your-average-prediction-market.

[3] http: /www. jerrydallal. com/LHSP/slr. htm.

[4] Tom Obremski: Pricing Models Using Real Data. Teaching Statistics, vol. 30(2), 2008, pp.2144-2148.

[5] Ihsan Korkut, Adem Acir and Mehmet Boy: Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining. Expert Systems with Applications, vol. 38(9), 2011, pp.11651-11656.

DOI: 10.1016/j.eswa.2011.03.044

[6] Lorenzo Cerretani, Rubén M. Maggio, Carlo Barnaba, and Tullia Gallina Toschi: Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil. Food Chemistry, vol. 127(4), 2011, p.1899-(1904).

DOI: 10.1016/j.foodchem.2011.02.041

[7] Suarez Sanchez A., Garcia Nieto P. J. and Riesgo Fernandez P.: Application of an SVM-based regression model to the air quality study at local scale in the Aviles urban area (Spain). Mathematical and Computer Modelling, vol. 54(5-6), 2011, pp.1453-1466.

DOI: 10.1016/j.mcm.2011.04.017

[8] Roberto Z. Freire, Gustavo H.C. Oliveira and Nathan Mendes: Development of regression equations for predicting energy and hygrothermal performance of buildings. Energy and Buildings, vol. 40, 2008, pp.810-820.

DOI: 10.1016/j.enbuild.2007.05.014

[9] Jay L. Devore: Probability and Statistics for Engineering and the Science, 6th ed., Brooks/Cole, 2004, p.496–554.

[10] Prem S. Mann: Introductory statistics, 5th ed., John Wiley & Sons, inc, 2004, p.580–642.