Relevant bibliographies by topics / Analysis correlation-regression

Academic literature on the topic 'Analysis correlation-regression'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Analysis correlation-regression"

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Bolshakova, Lyudmila Valentinovna. "Correlation and Regression Analysis of Economic Problems." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 2077–88. http://dx.doi.org/10.47059/revistageintec.v11i3.2074.

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Shi, Runhua, and Steven A. Conrad. "Correlation and regression analysis." Annals of Allergy, Asthma & Immunology 103, no. 4 (October 2009): S35—S41. http://dx.doi.org/10.1016/s1081-1206(10)60820-4.

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Jensen, A. L. "Functional Regression and Correlation Analysis." Canadian Journal of Fisheries and Aquatic Sciences 43, no. 9 (September 1, 1986): 1742–45. http://dx.doi.org/10.1139/f86-218.

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In fisheries, many applications of regression analysis are based on functional relations, but application of predictive regression results in two regression equations. Ricker proposed application of a method developed by Teissier to estimate the geometric mean functional relation when the parameters of a functional relation are of biological significance. Functional regression results in a single equation relating variables as opposed to the two equations that result when predictive regression is applied. The geometric mean functional relation also is given by bivariate normal correlation analysis when the correlation coefficient is 1. Bivariate normal correlation analysis provides a model for functional regression. An equation for variation of observed values about the functional regression line is obtained, and functional regression is compared with predictive regression. If the model assumptions are met, the one equation of functional regression is less precise for prediction than the two equations of predictive regression. However, the confidence intervals for the estimates of the slopes for functional and predictive regression are nearly the same.
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Kabir, Mir Md Jahangir, and Tansif Anzar. "Regression and Correlation Analysis of Different Interesting Measures for Mining Association Rules." International Journal of Innovative Research in Computer Science & Technology 6, no. 4 (July 2018): 62–68. http://dx.doi.org/10.21276/ijircst.2018.6.4.4.

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Abd El-Mohsen, A. A. "Correlation and Regression Analysis in Barley." Egyptian Journal of Plant Breeding 16, no. 3 (2012): 205–26. http://dx.doi.org/10.12816/0003958.

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Grigorieva, T., and V. Tolubaev. "Correlation-regression analysis of technological parameters." Systems. Methods. Technologies, no. 3(39) (2018): 57–61. http://dx.doi.org/10.18324/2077-5415-2018-3-57-61.

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Egoshin, V. L., S. V. Ivanov, N. V. Savvina, A. R. Ermolaev, S. A. Mamyrbekova, L. M. Zhamaliyeva, and А. М. Grjibovski. "CORRELATION AND SIMPLE REGRESSION ANALYSIS USING R." Human Ecology, no. 12 (December 19, 2018): 55–64. http://dx.doi.org/10.33396/1728-0869-2018-12-55-64.

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Kang, Hee Mo, and Songyong Sim. "Regression and Correlation Analysis via Dynamic Graphs." Communications for Statistical Applications and Methods 10, no. 3 (December 1, 2003): 695–705. http://dx.doi.org/10.5351/ckss.2003.10.3.695.

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Iminov, Tukhtasin, and Hakim Kurganov. "CORRELATION-REGRESSION ANALYSIS OF IRRIGATION AND RECOVERY." JOURNAL OF AGRO PROCESSING 4, no. 2 (April 30, 2020): 40–44. http://dx.doi.org/10.26739/2181-9904-2020-4-8.

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Joshi, Aparna, Ganesh Khemnar, and Medha Puranik. "ESTABLISHING THE CORRELATION BETWEEN FOOT LENGTH AND HEIGHT – REGRESSION ANALYSIS STUDY IN PUNE REGION." International Journal of Anatomy and Research 8, no. 1.2 (February 5, 2020): 7316–19. http://dx.doi.org/10.16965/ijar.2019.365.

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Dissertations / Theses on the topic "Analysis correlation-regression"

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Twagirumukiza, Etienne. "Analysis of Faculty Evaluation by Students as a Reliable Measure of Faculty Teaching Performance." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/105.

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Most American universities and colleges require students to provide faculty evaluation at end of each academic term, as a way of measuring faculty teaching performance. Although some analysts think that this kind of evaluation does not necessarily provide a good measurement of teaching effectiveness, there is a growing agreement in the academic world about its reliability. This study attempts to find any strong statistical evidence supporting faculty evaluation by students as a measure of faculty teaching effectiveness. Emphasis will be on analyzing relationships between instructor ratings by students and corresponding students’ grades. Various statistical methods are applied to analyze a sample of real data and derive conclusions. Methods considered include multivariate statistical analysis, principal component analysis, Pearson's correlation coefficient, Spearman's and Kendall’s rank correlation coefficients, linear and logistic regression analysis.
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Narteh, Alexander Tetteh. "Correlation of Fluorescence Spectroscopy and Biochemical Oxygen Demand (BOD5) Using Regression Analysis." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5567.

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This research uses Regression analysis of fluorescence spectroscopy results to correlate with Biochemical Oxygen Demand (BOD5). Fluorescence spectroscopy was applied to samples taken from seven sample sites in the Provo and Orem waste water treatment plants found in Utah County. A total of 161 samples were collected for this research. 23 samples each were taken from four sites in the Provo waste water treatment plant namely Provo head works, aeration basin, primary filter settlement basin and the Provo effluent basin. The Orem head works, the clarifier and the Orem effluent basin were the three sample sites in the Orem waste water treatment plant where 23 samples each were collected to carry out the analysis. The fluorescent characteristics of the samples were determined using fluorescence spectrometry. These intensities were correlated with standard five day Biochemical Oxygen Demand (BOD5) values which were used as a measure of the amount of biodegradable organic material present. Chemical oxygen demand (COD) data were also taken from these treatment plants for correlation purposes. Three different correlation analyses were made which were the correlation of fluorescence spectroscopy excitation-emission matrix (EEM) against (1) individual sites BOD and COD values (2) Provo only and Orem only BOD and COD values (3) combined Provo and Orem BOD and COD values. The correlation of Individual site EEMs against BOD and COD values produced the best results. There was a higher correlation of EEM with BOD data than COD data. The R-squared for the combined Provo and Orem BOD data was 0.756 and that for COD was 0.729. Very high R-squared was obtained for Provo Influent data and Orem Influent data which were 0.955 and 0.946 respectively. This method can be used by wastewater stakeholders in deriving quick results in determining potential pollution events within a shorter time frame. This research demonstrates that there is a correlation between EEM and BOD/COD.
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Lei, Song. "Informative correlation extraction from and for Forex market analysis." AUT University, 2010. http://hdl.handle.net/10292/899.

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The forex market is a complex, evolving, and a non-linear dynamical system, and its forecast is difficult due to high data intensity, noise/outliers, unstructured data and high degree of uncertainty. However, the exchange rate of a currency is often found surprisingly similar to the history or the variation of an alternative currency, which implies that correlation knowledge is valuable for forex market trend analysis. In this research, we propose a computational correlation analysis for the intelligent correlation extraction from all available economic data. The proposed correlation is a synthesis of channel and weighted Pearson's correlation, where the channel correlation traces the trend similarity of time series, and the weighted Pearson's correlation filters noise in correlation extraction. In the forex market analysis, we consider 3 particular aspects of correlation knowledge: (1) historical correlation, correlation to previous market data; (2) cross-currency correlation, correlation to relevant currencies, and (3) macro correlation, correlation to macroeconomic variables. While evaluating the validity of extracted correlation knowledge, we conduct a comparison of Support Vector Regression (SVR) against the correlation aided SVR (cSVR) for forex time series prediction, where correlation in addition to the observed forex time series data is used for the training of SVR. The experiments are carried out on 5 futures contracts (NZD/AUD, NZD/EUD, NZD/GBP, NZD/JPY and NZD/USD) within the period from January 2007 to December 2008. The comparison results show that the proposed correlation is computationally significant for forex market analysis in that the cSVR is performing consistently better than purely SVR on all 5 contracts exchange rate prediction, in terms of error functions MSE, RMSE, NMSE, MAE and MAPE. However, the cSVR prediction is found occasionally differing significantly from the actual price, which suggests that despite the significance of the proposed correlation, how to use correlation knowledge for market trend analysis remains a very challenging difficulty that prevents in practice further understanding of the forex market. In addition, the selection of macroeconomic factors and the determination of time period for analysis are two computationally essential points worth addressing further for future forex market correlation analysis.
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Wang, Guoshen. "Analysis of Additive Risk Model with High Dimensional Covariates Using Correlation Principal Component Regression." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/51.

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One problem of interest is to relate genes to survival outcomes of patients for the purpose of building regression models to predict future patients¡¯ survival based on their gene expression data. Applying semeparametric additive risk model of survival analysis, this thesis proposes a new approach to conduct the analysis of gene expression data with the focus on model¡¯s predictive ability. The method modifies the correlation principal component regression to handle the censoring problem of survival data. Also, we employ the time dependent AUC and RMSEP to assess how well the model predicts the survival time. Furthermore, the proposed method is able to identify significant genes which are related to the disease. Finally, this proposed approach is illustrated by simulation data set, the diffuse large B-cell lymphoma (DLBCL) data set, and breast cancer data set. The results show that the model fits both of the data sets very well.
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Sakwe, Chantal Wase. "Analysis of Suspended Sediment Loads in Streams and Rivers using Linear Regression and Pearson Correlation." Youngstown State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1452867844.

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Dastmard, Benjamin. "A statistical analysis of the connection between test results and field claims for ECUs in vehicles." Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-118428.

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The objective of this thesis is to analyse theconnection between test results and field claims of ECUs (electronic controlunits) at Scania in order to improve the acceptance criteria and evaluatesoftware testing strategies. The connection is examined through computation ofdifferent measures of dependencies such as the Pearson’s correlation, Spearman’srank correlation and Kendall’s tau. The correlations are computed from testresults in different ECU projects and considered in a predictive model based onlogistic regression. Numerical results indicate a weak connection between testresults and field claims. This is partly due to insufficient number of ECUprojects and the lack of traceability of field claims and test results. Themain conclusion confirms the present software testing strategy. Continuoussoftware release and testing results in a lower field claim and thus a betterproduct.
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Vrugtman, Rosanne. "Dimensions of Intuition first-round validation studies /." Diss., St. Louis, Mo. : University of Missouri--St. Louis, 2009. http://etd.umsl.edu/r3801.

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Lacko, Matej. "Analýza ekonomických ukazatelů vybrané firmy pomocí statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2018. http://www.nusl.cz/ntk/nusl-378337.

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The diploma thesis deals with the analysis of economic indicators of Technos a.s., using statistical methods and the evaluation of the current financial situation. The work contains a theoretical and practical part. The theoretical part describes selected economic indicators, regression analysis, time series and correlation analysis. In the practical part, the analysis of selected economic indicators will be carried out and then statistical methods will be used to determine the prediction for the next year and to reveal the dependence between the individual indicators. The last part of the thesis deals with proposals that will improve the financial situation of the company.
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Funa, Laura. "Customer Satisfaction Analysis." Thesis, Linköpings universitet, Statistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71707.

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The objective of this master thesis is to identify “key-drivers” embedded in customer satisfaction data. The data was collected by a large transportation sector corporation during five years and in four different countries. The questionnaire involved several different sections of questions and ranged from demographical information to satisfaction attributes with the vehicle, dealer and several problem areas. Various regression, correlation and cooperative game theory approaches were used to identify the key satisfiers and dissatisfiers. The theoretical and practical advantages of using the Shapley value, Canonical Correlation Analysis and Hierarchical Logistic Regression has been demonstrated and applied to market research.
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Roy, Subroto, University of Western Sydney, College of Law and Business, and School of Marketing. "Innovation generation in buyer-seller relationships." THESIS_CLAB_MAR_Roy_S.xml, 2001. http://handle.uws.edu.au:8081/1959.7/235.

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In recent years, a number of researchers have questioned the traditional notion of the producer as the sole generator of innovation in buyer seller relationships. Increasingly, innovation generation has been recognized as an outcome of interaction between a firm and various outside entities. According to this view, supplier involvement and alliances are a route to innovation generation. Clearly, business market relationships provide an important opportunity for interaction between buyer and seller. Despite this realization, only very limited research has focused on innovation generation in business-to-business relationships. To alleviate this important gap in literature, this thesis develops a conceptual model and hypotheses of innovation generation in business-to-buyer seller relationships. The research uses a combination of qualitative and quantitative techniques to examine the proposed theoretical model of innovation generation. A pilot case study is followed by development of and purification of measures using the IMP database on supplier customer interfirm relations in Europe and China. The hypotheses and model are tested using correlation and regression analysis. Results suggest that innovation generation is indeed facilitated by buyer seller interactions. Interaction also moderates the effect of other relationship and technology factors and type of innovation generated
Doctor of Philosophy (PhD) (Marketing)
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Books on the topic "Analysis correlation-regression"

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Vogt, W., and R. Johnson. Correlation and Regression Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2012. http://dx.doi.org/10.4135/9781446286104.

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Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. Madison, Wis: University of Wisconsin Press, 1993.

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Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. Madison, Wis: University of Wisconsin Press, 1994.

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Rönz, Bernd. Regressions- und Korrelationsanalyse: Grundlagen, Methoden, Beispiele. Wiesbaden: Gabler, 1992.

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Sheldon, Zedeck, ed. Data analysis for research designs: Analysis-of-variance and multiple regression/correlation approaches. New York: W.H. Freeman, 1989.

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Analyzing environmental data. Hoboken, NJ: Wiley, 2004.

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Plots, transformations, and regression: An introduction to graphical methods of diagnostic regression analysis. Oxford: Clarendon Press, 1985.

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Atkinson, A. C. Plots, transformations and regression: An introduction to graphical methods of diagnostic analysis. Oxford: Oxford University Press, 1985.

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Seo, Takashi. Testing equality of means and simultaneous confidence intervals in repeated measures with missing data. Toronto: University of Toronto, Dept. of Statistics, 1997.

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Keppel, Geoffrey. Data analysis for research designs. NY: Freeman, 1995.

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Book chapters on the topic "Analysis correlation-regression"

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Sarantakos, Sotirios. "Correlation — regression." In A Toolkit for Quantitative Data Analysis, 104–16. London: Macmillan Education UK, 2007. http://dx.doi.org/10.1007/978-1-137-03825-8_11.

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Davison, A. C. "Regression and Correlation." In Methods of Environmental Data Analysis, 79–137. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2920-6_3.

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Davison, A. C. "Regression and Correlation." In Methods of Environmental Data Analysis, 79–137. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9512-9_3.

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Reddy, M. Venkataswamy. "Correlation Analysis and Regression Analysis." In Statistical Methods in Psychiatry Research and SPSS, 163–76. Second edition. | Toronto ; New Jersey : Apple Academic Press, 2018.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-12.

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Lindley, D. V. "Regression and Correlation Analysis." In Time Series and Statistics, 237–43. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_30.

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Lindley, D. V. "Regression and Correlation Analysis." In The New Palgrave Dictionary of Economics, 1–6. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_1873-1.

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Rassel, Gary, Suzanne Leland, Zachary Mohr, and Elizabethann O’Sullivan. "Regression Analysis and Correlation." In Research Methods for Public Administrators, 449–85. Seventh Edition. | New York : Routledge, 2021. | Revised edition of Research methods for public administrators, 2017.: Routledge, 2020. http://dx.doi.org/10.4324/9780429319860-14.

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Mariappan, Perumal. "Correlation and Regression Analysis." In Statistics for Business, 107–37. Boca Raton, FL : CRC Press, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429443244-7.

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Lindley, D. V. "Regression and Correlation Analysis." In The New Palgrave Dictionary of Economics, 11455–60. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1873.

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Dubey, Umesh Kumar Bhayyalal, and D. P. Kothari. "Correlation and Regression Analysis." In Research Methodology, 225–38. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315167138-15.

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Conference papers on the topic "Analysis correlation-regression"

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Godzelikh, L., and D. Vetoshkin. "THE ENERGY DISPERSION ANALYSIS OF SOILS: CORRELATION-REGRESSION ANALYSIS." In Modern problems of physics education. Baskir State University, 2021. http://dx.doi.org/10.33184/mppe-2021-11-10.83.

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Saitov, M. "DATA PROCESSING OF PHYSICAL ANALYSIS OF SOILS: CORRELATION-REGRESSION ANALYSIS." In Modern problems of physics education. Baskir State University, 2021. http://dx.doi.org/10.33184/mppe-2021-11-10.92.

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Ozer, Ozge, and M. Gurhan Yalcin. "Correlation of chemical contents of Sutlegen (Antalya) bauxites and regression analysis." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0026731.

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Hagiwara, Shiho, Takumi Uezono, Takashi Sato, and Kazuya Masu. "Improvement of power distribution network using correlation-based regression analysis." In the 17th great lakes symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1228784.1228906.

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Kapoor, Prerna, Prem Kumar Singh, and Aswani Kumar Cherukuri. "IT Act Crime Pattern Analysis using Regression and Correlation Matrix." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9197835.

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Geraskina, A. A., and L. N. Yudaeva. "APPLICATION OF CORRELATION AND REGRESSION ANALYSIS TO FORECAST RUSSIA'S GDP." In Modern Technologies in Science and Education MTSE-2020. Ryazan State Radio Engineering University, 2020. http://dx.doi.org/10.21667/978-5-6044782-6-4-29-33.

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VOSS, H. U., and J. KURTHS. "ANALYSIS OF SPATIOTEMPORAL DATA WITH NONPARAMETRIC REGRESSION AND MAXIMAL CORRELATION." In 5th Experimental Chaos Conference. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812811516_0020.

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Su, Xuan. "Regression analysis of correlation between learning time and academic performance." In International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), edited by Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen, and Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2628198.

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Intani, Arvita. "The Optimizing Spacer Roller Size by Correlation and Regression Analysis." In Proceedings of the 1st International Conference on Economics Engineering and Social Science, InCEESS 2020, 17-18 July, Bekasi, Indonesia. EAI, 2021. http://dx.doi.org/10.4108/eai.17-7-2020.2302972.

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Mohd Khir, Nor Haniza, Marina Ismail, and Jasmin Ilyani Ahmad. "Persona Modelling via Correlation and Regression Analysis in CCI Gamification." In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2020. http://dx.doi.org/10.1109/icraie51050.2020.9358326.

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Reports on the topic "Analysis correlation-regression"

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Steed, Chad A., J. Edward SwanII, Patrick J. Fitzpatrick, and T. J. Jankun-Kelly. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1035521.

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Qian, Zhi-Wang, Xiao-Lei Du, Yu-Feng Shi, and Ying Yu. Correlation between Economic Security and Medical and Health Service with Regression Analysis. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2018. http://dx.doi.org/10.7546/crabs.2018.01.17.

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Qian, Zhi-Wang, Xiao-Lei Du, Yu-Feng Shi, and Ying Yu. Correlation between Economic Security and Medical and Health Service with Regression Analysis. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2018. http://dx.doi.org/10.7546/grabs2018.1.17.

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McPhedran, R., K. Patel, B. Toombs, P. Menon, M. Patel, J. Disson, K. Porter, A. John, and A. Rayner. Food allergen communication in businesses feasibility trial. Food Standards Agency, March 2021. http://dx.doi.org/10.46756/sci.fsa.tpf160.

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Background: Clear allergen communication in food business operators (FBOs) has been shown to have a positive impact on customers’ perceptions of businesses (Barnett et al., 2013). However, the precise size and nature of this effect is not known: there is a paucity of quantitative evidence in this area, particularly in the form of randomised controlled trials (RCTs). The Food Standards Agency (FSA), in collaboration with Kantar’s Behavioural Practice, conducted a feasibility trial to investigate whether a randomised cluster trial – involving the proactive communication of allergen information at the point of sale in FBOs – is feasible in the United Kingdom (UK). Objectives: The trial sought to establish: ease of recruitments of businesses into trials; customer response rates for in-store outcome surveys; fidelity of intervention delivery by FBO staff; sensitivity of outcome survey measures to change; and appropriateness of the chosen analytical approach. Method: Following a recruitment phase – in which one of fourteen multinational FBOs was successfully recruited – the execution of the feasibility trial involved a quasi-randomised matched-pairs clustered experiment. Each of the FBO’s ten participating branches underwent pair-wise matching, with similarity of branches judged according to four criteria: Food Hygiene Rating Scheme (FHRS) score, average weekly footfall, number of staff and customer satisfaction rating. The allocation ratio for this trial was 1:1: one branch in each pair was assigned to the treatment group by a representative from the FBO, while the other continued to operate in accordance with their standard operating procedure. As a business-based feasibility trial, customers at participating branches throughout the fieldwork period were automatically enrolled in the trial. The trial was single-blind: customers at treatment branches were not aware that they were receiving an intervention. All customers who visited participating branches throughout the fieldwork period were asked to complete a short in-store survey on a tablet affixed in branches. This survey contained four outcome measures which operationalised customers’: perceptions of food safety in the FBO; trust in the FBO; self-reported confidence to ask for allergen information in future visits; and overall satisfaction with their visit. Results: Fieldwork was conducted from the 3 – 20 March 2020, with cessation occurring prematurely due to the closure of outlets following the proliferation of COVID-19. n=177 participants took part in the trial across the ten branches; however, response rates (which ranged between 0.1 - 0.8%) were likely also adversely affected by COVID-19. Intervention fidelity was an issue in this study: while compliance with delivery of the intervention was relatively high in treatment branches (78.9%), erroneous delivery in control branches was also common (46.2%). Survey data were analysed using random-intercept multilevel linear regression models (due to the nesting of customers within branches). Despite the trial’s modest sample size, there was some evidence to suggest that the intervention had a positive effect for those suffering from allergies/intolerances for the ‘trust’ (β = 1.288, p<0.01) and ‘satisfaction’ (β = 0.945, p<0.01) outcome variables. Due to singularity within the fitted linear models, hierarchical Bayes models were used to corroborate the size of these interactions. Conclusions: The results of this trial suggest that a fully powered clustered RCT would likely be feasible in the UK. In this case, the primary challenge in the execution of the trial was the recruitment of FBOs: despite high levels of initial interest from four chains, only one took part. However, it is likely that the proliferation of COVID-19 adversely impacted chain participation – two other FBOs withdrew during branch eligibility assessment and selection, citing COVID-19 as a barrier. COVID-19 also likely lowered the on-site survey response rate: a significant negative Pearson correlation was observed between daily survey completions and COVID-19 cases in the UK, highlighting a likely relationship between the two. Limitations: The trial was quasi-random: selection of branches, pair matching and allocation to treatment/control groups were not systematically conducted. These processes were undertaken by a representative from the FBO’s Safety and Quality Assurance team (with oversight from Kantar representatives on pair matching), as a result of the chain’s internal operational restrictions.
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5

Brosh, Arieh, David Robertshaw, Yoav Aharoni, Zvi Holzer, Mario Gutman, and Amichai Arieli. Estimation of Energy Expenditure of Free Living and Growing Domesticated Ruminants by Heart Rate Measurement. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580685.bard.

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Research objectives were: 1) To study the effect of diet energy density, level of exercise, thermal conditions and reproductive state on cardiovascular function as it relates to oxygen (O2) mobilization. 2) To validate the use of heart rate (HR) to predict energy expenditure (EE) of ruminants, by measuring and calculating the energy balance components at different productive and reproductive states. 3) To validate the use of HR to identify changes in the metabolizable energy (ME) and ME intake (MEI) of grazing ruminants. Background: The development of an effective method for the measurement of EE is essential for understanding the management of both grazing and confined feedlot animals. The use of HR as a method of estimating EE in free-ranging large ruminants has been limited by the availability of suitable field monitoring equipment and by the absence of empirical understanding of the relationship between cardiac function and metabolic rate. Recent developments in microelectronics provide a good opportunity to use small HR devices to monitor free-range animals. The estimation of O2 uptake (VO2) of animals from their HR has to be based upon a consistent relationship between HR and VO2. The question as to whether, or to what extent, feeding level, environmental conditions and reproductive state affect such a relationship is still unanswered. Studies on the basic physiology of O2 mobilization (in USA) and field and feedlot-based investigations (in Israel) covered a , variety of conditions in order to investigate the possibilities of using HR to estimate EE. In USA the physiological studies conducted using animals with implanted flow probes, show that: I) although stroke volume decreases during intense exercise, VO2 per one heart beat per kgBW0.75 (O2 Pulse, O2P) actually increases and measurement of EE by HR and constant O2P may underestimate VO2unless the slope of the regression relating to heart rate and VO2 is also determined, 2) alterations in VO2 associated with the level of feeding and the effects of feeding itself have no effect on O2P, 3) both pregnancy and lactation may increase blood volume, especially lactation; but they have no effect on O2P, 4) ambient temperature in the range of 15 to 25°C in the resting animal has no effect on O2P, and 5) severe heat stress, induced by exercise, elevates body temperature to a sufficient extent that 14% of cardiac output may be required to dissipate the heat generated by exercise rather than for O2 transport. However, this is an unusual situation and its affect on EE estimation in a freely grazing animal, especially when heart rate is monitored over several days, is minor. In Israel three experiments were carried out in the hot summer to define changes in O2P attributable to changes in the time of day or In the heat load. The animals used were lambs and young calves in the growing phase and highly yielding dairy cows. In the growing animals the time of day, or the heat load, affected HR and VO2, but had no effect on O2P. On the other hand, the O2P measured in lactating cows was affected by the heat load; this is similar to the finding in the USA study of sheep. Energy balance trials were conducted to compare MEI recovery by the retained energy (RE) and by EE as measured by HR and O2P. The trial hypothesis was that if HR reliably estimated EE, the MEI proportion to (EE+RE) would not be significantly different from 1.0. Beef cows along a year of their reproductive cycle and growing lambs were used. The MEI recoveries of both trials were not significantly different from 1.0, 1.062+0.026 and 0.957+0.024 respectively. The cows' reproductive state did not affect the O2P, which is similar to the finding in the USA study. Pasture ME content and animal variables such as HR, VO2, O2P and EE of cows on grazing and in confinement were measured throughout three years under twenty-nine combinations of herbage quality and cows' reproductive state. In twelve grazing states, individual faecal output (FO) was measured and MEI was calculated. Regression analyses of the EE and RE dependent on MEI were highly significant (P<0.001). The predicted values of EE at zero intake (78 kcal/kgBW0.75), were similar to those estimated by NRC (1984). The EE at maintenance condition of the grazing cows (EE=MEI, 125 kcal/kgBW0.75) which are in the range of 96.1 to 125.5 as presented by NRC (1996 pp 6-7) for beef cows. Average daily HR and EE were significantly increased by lactation, P<0.001 and P<0.02 respectively. Grazing ME significantly increased HR and EE, P<0.001 and P<0.00l respectively. In contradiction to the finding in confined ewes and cows, the O2P of the grazing cows was significantly affected by the combined treatments (P<0.00l ); this effect was significantly related to the diet ME (P<0.00l ) and consequently to the MEI (P<0.03). Grazing significantly increased O2P compared to confinement. So, when EE of grazing animals during a certain season of the year is estimated using the HR method, the O2P must be re measured whenever grazing ME changes. A high correlation (R2>0.96) of group average EE and of HR dependency on MEI was also found in confined cows, which were fed six different diets and in growing lambs on three diets. In conclusion, the studies conducted in USA and in Israel investigated in depth the physiological mechanisms of cardiovascular and O2 mobilization, and went on to investigate a wide variety of ruminant species, ages, reproductive states, diets ME, time of intake and time of day, and compared these variables under grazing and confinement conditions. From these combined studies we can conclude that EE can be determined from HR measurements during several days, multiplied by O2P measured over a short period of time (10-15 min). The study showed that RE could be determined during the growing phase without slaughtering. In the near future the development microelectronic devices will enable wide use of the HR method to determine EE and energy balance. It will open new scopes of physiological and agricultural research with minimizes strain on animals. The method also has a high potential as a tool for herd management.
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