Relevant bibliographies by topics / Spearman's rank correlation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spearman's rank correlation'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Spearman's rank correlation"

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Chambers, Ll G. "73.52 Spearman's Rank Correlation Coefficient." Mathematical Gazette 73, no. 466 (December 1989): 331. http://dx.doi.org/10.2307/3619313.

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Burghes, David N. "Teaching Spearman's Rank Correlation Coefficient." Teaching Statistics 15, no. 3 (September 1993): 68–69. http://dx.doi.org/10.1111/j.1467-9639.1993.tb00658.x.

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Lovie, A. D. "Who discovered Spearman's rank correlation?" British Journal of Mathematical and Statistical Psychology 48, no. 2 (November 1995): 255–69. http://dx.doi.org/10.1111/j.2044-8317.1995.tb01063.x.

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FUEDA, Kaoru. "SPEARMAN'S RANK CORRELATION TYPE TWO-SAMPLE TEST." Memoirs of the Faculty of Science, Kyusyu University. Series A, Mathematics 47, no. 1 (1993): 27–39. http://dx.doi.org/10.2206/kyushumfs.47.27.

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Ramsey, Philip H. "Critical Values for Spearman's Rank Order Correlation." Journal of Educational Statistics 14, no. 3 (1989): 245. http://dx.doi.org/10.2307/1165017.

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Gauthier, T. "Detecting Trends Using Spearman's Rank Correlation Coefficient." Environmental Forensics 2, no. 4 (December 2001): 359–62. http://dx.doi.org/10.1006/enfo.2001.0061.

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Gautheir, Thomas D. "Detecting Trends Using Spearman's Rank Correlation Coefficient." Environmental Forensics 2, no. 4 (January 2001): 359–62. http://dx.doi.org/10.1080/713848278.

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Berry, Kenneth J., and Paul W. Mielke. "Spearman's Footrule as a Measure of Agreement." Psychological Reports 80, no. 3 (June 1997): 839–46. http://dx.doi.org/10.2466/pr0.1997.80.3.839.

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Spearman's footrule measure of the relationship between two sets of ranks is shown to be a chance-corrected measure of agreement. The footrule is generalized to include tied ranks and a comparison with Spearman's rank-order correlation coefficient is provided. Procedures to determine the nonasymptotic probability of the footrule with tied ranks are presented.
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Henery, R. J. "Asymptotic convergence in distribution for spearman's rank correlation coefficient." Communications in Statistics - Simulation and Computation 17, no. 4 (January 1988): 1449–52. http://dx.doi.org/10.1080/03610918808812734.

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Liu, Qi, Chun Li, Valentine Wanga, and Bryan E. Shepherd. "Covariate-adjusted Spearman's rank correlation with probability-scale residuals." Biometrics 74, no. 2 (November 13, 2017): 595–605. http://dx.doi.org/10.1111/biom.12812.

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Dissertations / Theses on the topic "Spearman's rank correlation"

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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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Michal, Ondřej. "Stanovení hodnot materiálových parametrů s využitím experimentů různých konfigurací." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2012. http://www.nusl.cz/ntk/nusl-225579.

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The work occupy by inverse analysis based on artificial neural network. This identification algorithm enable correct determine parameters of applied material model on creation of numerical model of construction so it's possible that the results of computerized simulation correspond with experiments. It look's like suitable approach especially in cases with complicated problems and complex models with many material parameters.
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CHEN, ZHAOREN. "ANALYSIS OF EQUITY MARKETS: A SPEARMAN RANK CORRELATION COEFFICIENT APPROACH." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612636.

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In this honors thesis, I attempted to develop an investment strategy to analyze the U.S. stock market by studying the correlations between nine sectors defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR). These funds are a group of exchange-traded funds (ETFs) which are traded worldwide and can adequately represent the performance of the sectors. Through my method, I hope to identify the sector trends in the future by evaluating the sector ETF’s most recent short-term period data. This analysis will provide an alternate investment method for investors to avoid short-term investment risk as well as explanations of why I think the approach may not be valid.
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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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Louw, Charl. "The presentation of self-concept and emotional profile in a cardiological population." Diss., Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-02092005-092504.

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Liebenberg, Andre. "The relationship between economic freedom, political freedom and economic growth." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/30619.

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The research aims to investigate the relationship between economic freedom, political freedom and economic growth. The Arab Spring placed renewed interest on the topic of freedom, yet current economic conditions seemingly contradicted the established theory. The largest free economies were being outperformed by those with less political and economic freedom.Three objectives were specified to answer the research question. The first objective aimed to determine the association between economic freedom, political freedom and economic growth, for which Spearman’s correlation was used. The second objective aimed to investigate causal relationships between the variables, for which Granger’s causality was employed. The third objective aimed to examine complex relationships between the variables, for which vector autoregression was used.Economic growth was weakly correlated with the independent variables. Civil liberties, political rights and economic freedom, however, had strong correlations with each other. Economic freedom and economic growth had bi-directional Granger-causality. Political rights Granger-caused economic freedom whilst civil liberties Granger-caused political rights and economic freedom. Using vector autoregression, the model consisting of economic growth, economic freedom and civil liberties had the greatest explanatory power towards economic growth. Existing theory therefore remains valid: political freedom enhances economic freedom, which, in turn, enhances economic growth.The relationship between economic freedom, political freedom and economic growth
Dissertation (MBA)--University of Pretoria, 2012.
Gordon Institute of Business Science (GIBS)
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HUANG, SHIJING. "ESTIMATING ILLEGAL DRUG MARKET LOCATION IN CINCINNATI USING THE HUFF MODEL." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1090862711.

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Barnes, Samantha Jones. "Characterization of the Serologic Responses to Plasmodium vivax DBPII Variants Among Inhabitants of Pursat Province, Cambodia." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3970.

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The Plasmodium vivax Duffy Binding Protein (DBP) is the ligand in the major pathway for P. vivax invasion of human reticulocytes, making it an appealing vaccine candidate. Region II of DBP (DBP-RII) is the minimal portion of the ligand that mediates recognition of the Duffy Antigen Receptor for Chemokines (DARC receptor) on the reticulocyte surface and constitutes the primary vaccine target. Analysis of natural variation in the coding sequences of DBP-RII revealed signature evidence for selective pressure driving variation in the residues of the putative receptor-binding site. We hypothesize that anti-DBP immunity in P. vivax infections is strain-specific and hindered by polymorphic residues altering sensitivity to immune antibody inhibition. To comprehend the human IgG response following P. vivax infections we investigated the specificity of IgG in Pursat Province, Western Cambodia. Using ELISAs, we quantified the antibody titer against five variant alleles of DBP-RII. We also sequenced the DBP-RII of the field isolates to determine their relationship to the variant alleles used in the ELISAs. When correlating the IgG titer between the DBP variants a strain-specific immune response was observed in patients with a high antibody titer to DBP-RII_AH as compared to the other variants. This was different from the correlation of high antibody titers between DBP-RII_P and DBP-RII_7.18 (ρ=0.88, p-value<0.0001) and DBP-RII_P and DBP-RII_O (ρ=0.87, p-value<0.0001). There appeared to be little correlation between specific polymorphic residues and IgG titer. Understanding the immune response to the polymorphisms within PvDBP will allow further identification of epitopes to enable the production of a more effective P. vivax vaccine
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VOMÁČKA, Pavel. "Možnosti promítnutí rizika do hodnocení finanční situace podniku." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-174018.

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Objective of this Master's thesis is to evaluate different ways of measuring financial performance of enterprise. Economic Value Added is compared with older, traditional indicators of profitability. Emphasis lies on general applicability. In theoretical part, history of entrepreneurial goals is in addition to the actual analysis of tested indicators outlined. EVA, profitability ratios are assessed using a sample of 100 Czech manufacturing companies. In practical part, outputs of thesis apart from actual testing are stated.
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Books on the topic "Spearman's rank correlation"

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Sloan, Luke, and Rob Angell. Spearman’s Rank-Order Correlation Coefficient and the General Social Survey (2012): Income and Political Influence. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937987.

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Sloan, Luke, and Rob Angell. Spearman’s Rank-Order Correlation Coefficient and the General Social Survey (2012): Highest Level of Education and Income. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937857.

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Spearman's Rank-Order Correlation Coefficient and the World DataBank (2012): Gross Domestic Product Per Capita and Internet Access. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937871.

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Spearman’s Rank-Order Correlation Coefficient and the Global Health Observatory Data (2012): Life Expectancy and Per Capita Government Health Spending. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473948181.

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Book chapters on the topic "Spearman's rank correlation"

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Gooch, Jan W. "Spearman Rank Correlation Coefficient." In Encyclopedic Dictionary of Polymers, 996–97. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15382.

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MacFarland, Thomas W., and Jan M. Yates. "Spearman’s Rank-Difference Coefficient of Correlation." In Introduction to Nonparametric Statistics for the Biological Sciences Using R, 249–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30634-6_8.

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Grzegorzewski, Przemysław, and Paulina Ziembińska. "Spearman’s Rank Correlation Coefficient for Vague Preferences." In Flexible Query Answering Systems, 342–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24764-4_30.

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Kokoska, Stephen, and Christopher Nevison. "Critical Values For Spearman’s Rank Correlation Coefficient." In Springer Texts in Statistics, 86. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9629-1_22.

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Williams, R. B. G. "Spearman’s and Kendall’s Coefficients of Rank Correlation." In Intermediate Statistics for Geographers and Earth Scientists, 451–79. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-06813-5_6.

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Baszczyńska, Aleksandra, and Czesław Domański. "Distribution of the Spearman’s Rank Correlation Coefficient." In Operations Research ’92, 333–35. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-662-12629-5_95.

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Shah, Dharambhai, and Tanish Zaveri. "Hyperspectral Endmember Extraction Algorithm Using Spearman’s Rank Correlation." In Algorithms for Intelligent Systems, 305–18. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5077-5_27.

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Khare, Shreya, Akshay Bhandari, Saurabh Singh, and Anuja Arora. "ECG Arrhythmia Classification Using Spearman Rank Correlation and Support Vector Machine." In Advances in Intelligent and Soft Computing, 591–98. New Delhi: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0491-6_54.

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Dancelli, Livia, Marica Manisera, and Marika Vezzoli. "On Two Classes of Weighted Rank Correlation Measures Deriving from the Spearman’s ρ." In Statistical Models for Data Analysis, 107–14. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00032-9_13.

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Zhao, Boxu, and Guiming Luo. "Reasoning of Causal Direction in Linear Model Based on Spearman’s Rank Correlation Coefficient." In Knowledge Science, Engineering and Management, 259–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29563-9_24.

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Conference papers on the topic "Spearman's rank correlation"

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Fan, Xiao, Qi Wang, Kaiyuan Liu, Min Li, and Pu Liu. "A coherence algorithm based on Spearman's rank correlation." In SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, 2018. http://dx.doi.org/10.1190/segam2018-2996512.1.

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Hernández-Aguirre, Arturo, Enrique Villa-Diharce, and Selma Barba-Moreno. "An estimation distribution algorithm with the spearman's rank correlation index." In the 10th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389182.

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Ferdous, Rayhan, Md Zahidul Islam, and Ferdous Bin Ali. "Identifying miRNA-mRNA interactions by a combination of spearman's rank correlation and IDA." In 2016 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2016. http://dx.doi.org/10.1109/iciev.2016.7760131.

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Dulkarnaev, Marat Rafailevich, Evgeny Alexandrovich Malyavko, Ekaterina Evgenievna Semyonova, Oksana Alexandrovna Gorbokonenko, Yuri Alexeyevich Kotenev, Shamil Khanifovich Sultanov, Alexander Viacheslavovich Chibisov, and Daria Yuryevna Chudinova. "The Use of Quantum Dot Inflow Tracers in Multi-Well Reservoir Production Surveillance and Inter-Well Diagnostics." In SPE Symposium: Petrophysics XXI. Core, Well Logging, and Well Testing. SPE, 2021. http://dx.doi.org/10.2118/208430-ms.

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Abstract Reservoir pressure maintenance is an extremely important factor in field development. In enhanced oil recovery water flooding projects, it is essential to optimize the flooding efficiency in a timely manner and reduce uncertainties in inter-well hydrodynamic modelling. Usually, the inter-well space parameters are assessed using interference tests or tracer- based surveillance. These methods offer such advantages as reliable information on the flow communication in the target area and the reservoir connectivity in different zones of the field. However, the duration and cost of the described surveillance technologies pose a significant drawback, and therefore alternative physical and mathematical methods with simplified forecast models are widely spread. This paper describes a method for integrating the results of dynamic marker-based inflow production surveillance in horizontal wells and the Spearman's rank-order correlation method. This approach is applied to provide better interventions for reservoir pressure maintenance, optimization of in-fill drilling, update existing hydro-dynamic models and reduce the level of uncertainty in decision making.
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Szmidt, Eulalia, and Janusz Kacprzyk. "The Spearman rank correlation coefficient between intuitionistic fuzzy sets." In 2010 5th IEEE International Conference Intelligent Systems (IS). IEEE, 2010. http://dx.doi.org/10.1109/is.2010.5548399.

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Dewalle, A. S., N. Betrouni, M. Steinling, M. Vermandel, J. Rousseau, and C. Vasseur. "Comparison between shifted Spearman rank correlation test and SPM in fMRI." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353061.

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Szmidt, Eulalia, and Janusz Kacprzyk. "The Spearman and Kendall rank correlation coefficients between intuitionistic fuzzy sets." In 7th conference of the European Society for Fuzzy Logic and Technology. Paris, France: Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.85.

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Spicer, C., and H. Gangloff. "241. Random Failure in Appplication of Spearman Rank Correlation to Bioaerosol Sampling Data." In AIHce 1999. AIHA, 1999. http://dx.doi.org/10.3320/1.2763085.

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Wang, Wei, Wei Qi, Nengcheng Chen, Peng Wang, and Jiaying Chen. "Research on determining the weights of urban land grading evaluation factors based on Spearman rank correlation analysis." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837404.

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Kairiene, Aida. "The Relationship between English Language Skills and Learning Needs of Secondary School Students." In 14th International Scientific Conference "Rural Environment. Education. Personality. (REEP)". Latvia University of Life Sciences and Technologies. Faculty of Engineering. Institute of Education and Home Economics, 2021. http://dx.doi.org/10.22616/reep.2021.14.010.

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Students learn English with certain motives. Many students seek to improve their careers, others seek to gain confidence, and others plan to live abroad. Thus, the problematic question arises: how do English language skills and learning needs of secondary school students relate to each other? The research aim is to designate the relationship between English language skills and learning needs of secondary school students. The methods of descriptive statistics were used. The Spearman rank correlation coefficient was applied. Mostly the medium correlation and a weak correlation were dominated between English language skills and learning needs. The analysis disclosed that students give more priority to receptive skills ‒ reading and listening than to communicative - speaking and writing skills.
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Reports on the topic "Spearman's rank correlation"

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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.

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30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).
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