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Статті в журналах з теми "Analysis correlation-regression"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

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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2

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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3

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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Анотація:

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Yakovleva, Tatyana A. "MANAGEMENT OF MIGRATORY PROCESSES USING CORRELATION REGRESSION ANALYSIS." Scholarly Notes of Komsomolsk-na-Amure State Technical University 2, no. 12 (December 30, 2012): 98–102. http://dx.doi.org/10.17084/2012.iv-2(12).18.

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12

DAUDPOTO, M. R., M. G. H. TALPUR, A. KHOOHARO, and K. ARAIN. "Regression and correlation analysis on rotating biological contactor." SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES 49, no. 004 (December 19, 2017): 849——852. http://dx.doi.org/10.26692/surj/2017.12.70.

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13

Meghanathan, Natarajan, and Xiaojia He. "Correlation and Regression Analysis for Node Betweenness Centrality." International Journal in Foundations of Computer Science & Technology 6, no. 6 (November 30, 2016): 01–20. http://dx.doi.org/10.5121/ijfcst.2016.6601.

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14

Algina, James, and Stephen Olejnik. "Sample Size Tables for Correlation Analysis with Applications in Partial Correlation and Multiple Regression Analysis." Multivariate Behavioral Research 38, no. 3 (July 2003): 309–23. http://dx.doi.org/10.1207/s15327906mbr3803_02.

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15

G, Vinitha, and Mallikarjun M. "CORRELATION AND REGRESSION ANALYSIS OF STATURE IN RELATION TO HEAD LENGTH IN CHILDERN IN NORTH KARNATAKA." International Journal of Anatomy and Research 5, no. 3.3 (September 30, 2017): 4321–25. http://dx.doi.org/10.16965/ijar.2017.244.

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16

Gao, H. "How to do linear correlation and regression analysis correctly." Journal of Chinese Integrative Medicine 6, no. 12 (December 15, 2008): 1311–14. http://dx.doi.org/10.3736/jcim20081221.

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17

I. Marie, Abdul-Jabbar. "CORRELATION , PATH COEFFICIENTS AND REGRESSION ANALYSIS IN SUMMER SQUASH." Mesopotamia Journal of Agriculture 38 (April 28, 2010): 54–52. http://dx.doi.org/10.33899/magrj.2010.33573.

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18

Wang, Mingliang, Michael B. Kane, and Dehai Zhao. "Correlation-Regression Analysis for Understanding Dominant Height Projection Accuracy." Forest Science 63, no. 6 (December 28, 2017): 549–58. http://dx.doi.org/10.5849/fs-2016-092r2.

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19

Zeng, Bilin, Kang Chen, and Cong Wang. "Geometric Views of Partial Correlation Coefficient in Regression Analysis." International Journal of Statistics and Probability 6, no. 3 (May 14, 2017): 51. http://dx.doi.org/10.5539/ijsp.v6n3p51.

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Анотація:

By describing the geometric analogues of the concepts from various perspectives, this work aims to provide a richer and intuitive comprehension of the concept of partial correlation coefficient in the regression analysis, especially for beginning students. Based on a simple and strictly correct geometric framework, this article geometrically illustrates the concept of partial correlation coefficient in regression analysis from the views of the Frisch-Waugh-Lovell Theorem, partial F test statistics, and the comparisons with other levels of correlation coefficients. In our opinion, the geometric approach sheds lights on the regression analysis as it provides a richer and more concrete understanding for readers, especially for beginners. This paper can also be served as a supplementary reading material for serious beginners.

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20

Fong, Geoffrey T., Jacob Cohen, and Patricia Cohen. "Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences." Journal of the American Statistical Association 80, no. 390 (June 1985): 485. http://dx.doi.org/10.2307/2287930.

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21

Sun, Jianguo. "A correlation principal component regression analysis of NIR data." Journal of Chemometrics 9, no. 1 (January 1995): 21–29. http://dx.doi.org/10.1002/cem.1180090104.

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22

Kemp, Freda. "Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 4 (December 2003): 691. http://dx.doi.org/10.1046/j.1467-9884.2003.t01-2-00383_4.x.

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23

An, Baiguo, Jianhua Guo, and Hansheng Wang. "Multivariate regression shrinkage and selection by canonical correlation analysis." Computational Statistics & Data Analysis 62 (June 2013): 93–107. http://dx.doi.org/10.1016/j.csda.2012.12.017.

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24

Kozyra, Cyprian. "Correlation and regression models in the Dyadic Data Analysis." Śląski Przegląd Statystyczny 18, no. 24 (2020): 263–67. http://dx.doi.org/10.15611/sps.2020.18.17.

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25

Domenko, Yurii Yu. "CORRELATION AND REGRESSION ANALYSIS AS A STRATEGIC PLANNING TOOL." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3, no. 12 (2021): 21–26. http://dx.doi.org/10.36871/ek.up.p.r.2021.12.03.004.

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Анотація:

Today, both scientific literature and legal acts define various tools for strategic planning of sоciо-economic development of the region (municipalities). At the same time, a single effective system has not yet been formed. In this study, the author proposes to include correlation and regression analysis as a tool in the sуstem of strategic planning of socio-economic development of the region. This strategic planning tool will allow you to make calculations when determining promising areas. It will also allow assessing their effectiveness in investing public funds in the development of the economy in the sectoral context, as well as the effectiveness of the implementation of national and regional projects and programs.

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26

Nizamova, G. Z., and M. M. Gaifullina. "CORRELATION AND REGRESSION ANALYSIS OF THE AUTOMOTIVE GASOLINE MARKET." Bulletin USPTU Science education economy Series economy 3, no. 37 (2021): 35–44. http://dx.doi.org/10.17122/2541-8904-2021-3-37-35-44.

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Анотація:

Purpose of the study: to identify the factors affecting the volume of production of motor gasoline. Research methods: analysis and synthesis, systematic approach, as well as methods of correlation and regression analysis. Results of the research: A methodological approach to the use of tools for correlation and regression analysis of the gasoline market is proposed, which includes the following stages: 1) formation of a data array; 2) carrying out correlation analysis, building a correlation matrix, selecting factors into the model using the Correlation tool in the Data Analysis package of MS Excel; 3) conducting a regression analysis, constructing a regression equation, substantiating the obtained dependence using the "Regression" tool in the "Data Analysis" MS Excel package, calculating the elasticity coefficients. It is proposed to use the volume of production of motor gasoline as effective in carrying out the correlation-regression analysis and constructing mathematical models. Among the dependent variables and factors affecting the volume of production of motor gasoline, it is proposed to use variables that characterize four groups of factors: resource (raw material) limitations (the volume of oil production and refining), production capabilities of the industry (through the depth of oil refining and the yield of light oil products that characterize production capacity and set of installations in the industry), price attractiveness of the market (prices of producers and consumers of motor gasoline, world oil prices), export attractiveness (volume and value of exports). Multivariate economic and statistical models of the dependence of the volume of production of motor gasoline on a number of factors have been developed. Based on the results of calculations, a strong correlation was revealed between the volume of production of motor gasoline and the values of primary oil refining, oil production, and export of motor gasoline. The predicted values are located as close as possible to the residual values, which indicates that the resulting regression equation has a high degree of accuracy. Research prospects: the research results can be used to identify significant factors in the development of the motor gasoline market in the Russian Federation.

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27

Pietrzak, Michał Bernard. "The Modifiable Areal Unit Problem – Analysis of Correlation and Regression." Equilibrium 9, no. 4 (December 31, 2014): 113–31. http://dx.doi.org/10.12775/equil.2014.028.

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Анотація:

The paper focuses on the issue of the modifiable areal unit problem, which means a possibility of obtaining various results for spatial economic analyses depending on the assumed composition of territorial units. The major research objective of the work is to examine the scale problem that constitutes one of the aspects of the modifiable areal unit problem. Analysis of the scale problem will be conducted for two research problems, namely, for the problem of the causal relationships between the level of investment outlays in enterprises per capita and the number of entities of the national economy per capita, and the issue of the dependence between the registered unemployment rate and the level of investment outlays per capita. The calculations based on the empirical values of those variables have showed that moving to a higher level of aggregation resulted in a change in the estimates of the parameters. The results obtained were the justification for undertaking the realisation of the objective. The scale problem was considered by means of a simulation analysis with a special emphasis laid on differentiating the variables expressed in absolute quantities and ones expressed in relative quantities. The study conducted allowed the identification of changes in basic properties as well as in correlation of the researched variables expressed in absolute and relative quantities. Based on the findings, it was stated that a correlation analysis and a regression analysis may lead to different conclusions depending on the assumed level of aggregation. The realisation of the research objective set in the paper also showed the need to consider the adequate character of variables in both spatial economic analyses and during the examination of the scale problem.

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28

Næs, Tormod, Oliver Tomic, Nils Kristian Afseth, Vegard Segtnan, and Ingrid Måge. "Multi-block regression based on combinations of orthogonalisation, PLS-regression and canonical correlation analysis." Chemometrics and Intelligent Laboratory Systems 124 (May 2013): 32–42. http://dx.doi.org/10.1016/j.chemolab.2013.03.006.

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29

Cho, Dong Sook, ChaeWeon Chung, Jeungim Kim, Sukhee Ahn, SoMi Park, and Hye Sook Park. "Analysis on Reports of Statistical Testings for Correlation and Regression." Korean Journal of Women Health Nursing 14, no. 3 (2008): 213. http://dx.doi.org/10.4069/kjwhn.2008.14.3.213.

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30

Razak, F. A., N. Baharun, N. A. Deraman, and N. R. P. Ismail. "Assessing students’ abilities in interpreting the correlation and regression analysis." Journal of Fundamental and Applied Sciences 9, no. 5S (January 19, 2018): 644. http://dx.doi.org/10.4314/jfas.v9i5s.45.

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31

Карпець, Костянтин Михайлович. "Regression-correlation analysis of morphological and morphometric parameters relief catchment." Technology audit and production reserves 6, no. 1(8) (December 11, 2012): 43–44. http://dx.doi.org/10.15587/2312-8372.2012.5470.

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32

Kavitha, S., T. Sabhanayagham, and R. Thenmozhi. "Analysis of Body Mass Index Based on Correlation and Regression." Research Journal of Pharmacy and Technology 11, no. 6 (2018): 2243. http://dx.doi.org/10.5958/0974-360x.2018.00415.8.

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33

HAN, Bing-Jin. "Correlation and regression analysis of trait and yield of soybean." CHINESE JOURNAL OF ECO-AGRICULTURE 16, no. 6 (March 2, 2009): 1429–33. http://dx.doi.org/10.3724/sp.j.1011.2008.01429.

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34

Rothstein, Hannah R., Michael Borenstein, Jacob Cohen, and Simcha Pollack. "Statistical Power Analysis for Multiple Regression/Correlation: A Computer Program." Educational and Psychological Measurement 50, no. 4 (December 1990): 819–30. http://dx.doi.org/10.1177/0013164490504009.

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35

Birth, Gerald S. "Evaluation of Correlation Coefficients Obtained with a Stepwise Regression Analysis." Applied Spectroscopy 39, no. 4 (July 1985): 729–32. http://dx.doi.org/10.1366/0003702854250031.

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36

Lutz, J. Gary, and Tanya L. Eckert. "The Relationship between Canonical Correlation Analysis and Multivariate Multiple Regression." Educational and Psychological Measurement 54, no. 3 (September 1994): 666–75. http://dx.doi.org/10.1177/0013164494054003009.

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37

Anderson, T. W. "Canonical correlation analysis and reduced rank regression in autoregressive models." Annals of Statistics 30, no. 4 (August 2002): 1134–54. http://dx.doi.org/10.1214/aos/1031689020.

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38

Glynn, Robert J. "Accounting for the Correlation Between Fellow Eyes in Regression Analysis." Archives of Ophthalmology 110, no. 3 (March 1, 1992): 381. http://dx.doi.org/10.1001/archopht.1992.01080150079033.

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39

Tsumoto, Yuko, and Shusaku Tsumoto. "Correlation and Regression Analysis for Characterizations of a University Hospital." Review of Socionetwork Strategies 5, no. 2 (December 2011): 43–55. http://dx.doi.org/10.1007/s12626-010-0020-z.

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40

Harper, Geoffrey. "A Correlation and Regression Approach to Phenology." Sibbaldia: the International Journal of Botanic Garden Horticulture, no. 7 (October 31, 2009): 139–57. http://dx.doi.org/10.24823/sibbaldia.2009.165.

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Анотація:

In this paper the developmental stages leading up to flowering are reviewed. It is then shown how correlation and regression methods of analysis can be used to identify the influence of air temperature at different developmental stages, using snowdrop and clover as case studies. Hypotheses are proposed to account for the findings, and it is suggested how such hypotheses could be tested. The implications for the construction of a functional phenological classification of plants are discussed.

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41

Hallinan, Maureen T., Geoffrey Keppel, and Sheldon Zedeck. "Data Analysis for Research Designs: Analysis of Variance and Multiple Regression/Correlation Approaches." Contemporary Sociology 19, no. 3 (May 1990): 486. http://dx.doi.org/10.2307/2072532.

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42

Hamer, Robert M., Geoffrey Keppel, and Sheldon Zedeck. "Data Analysis for Research Designs: Analysis of Variance and Multiple Regression/Correlation Approaches." Journal of the American Statistical Association 85, no. 409 (March 1990): 268. http://dx.doi.org/10.2307/2289579.

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43

Grigalashvili, A. S., L. F. Koksharova, and I. O. Zuyeva. "Application of Correlation Analysis to Exclude Factors from Regression-Differential Models." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 22, no. 1 (2016): 035–44. http://dx.doi.org/10.17277/vestnik.2016.01.pp.035-044.

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44

Lapach, S. M. "Determination of emissions in the correlation and one-dimensional regression analysis." Mathematical machines and systems 4 (2019): 126–38. http://dx.doi.org/10.34121/1028-9763-2019-4-126-138.

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45

Lin, Nan, Guochang Wang, and Baoxue Zhang. "Dimension reduction in functional regression using mixed data canonical correlation analysis." Statistics and Its Interface 6, no. 2 (2013): 187–96. http://dx.doi.org/10.4310/sii.2013.v6.n2.a3.

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46

., Ikram-ul-Haq, M. M. Gilani ., and F. A. Khan . "Correlation and Regression Analysis of Some Quantitative Characters Among Brassica Accessions." Pakistan Journal of Biological Sciences 1, no. 2 (March 15, 1998): 124–26. http://dx.doi.org/10.3923/pjbs.1998.124.126.

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47

Bhusal, SJ, and RB Thapa. "Response of Colony Strength to Honey Production: Regression and Correlation Analysis." Journal of the Institute of Agriculture and Animal Science 27 (May 1, 2006): 133–37. http://dx.doi.org/10.3126/jiaas.v27i0.706.

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Анотація:

An experiment was conducted using Randomized Complete Block (RCB) design to find the effects of initial colony strength of Apis mellifera L. on honey production with four levels of initial populations: 4 frames (9,800), 6 frames (14,700), 8 frames (19,600) and 10 frames (24,500) of adult honeybees per colony replicating 5 times during litchi flowering season (February to April, 2003) in Chitwan. This study revealed the exponential increase in honey production {Y = 4.22 – 0.7325 X + 0.09625 X2 (4 ≤X ≤ 10 comb covered by bees per colony) and Y = 4.22 – 0.000299 X + 0.000000016 X2 (9,800 ≤ X ≤ 24,500 honeybees per colony), n = 4} producing 2.32, 1.59 and 1.18 times more honey from initially 10-, 8- and 6-frame colonies as compared to the honey production of 2.82 kg/colony from 4-frame colonies. There was significantly positive linear correlation among colony attributes like colony strength, brood rearing, comb building, foraging activity and honey production. Key words: Apis mellifera, honey bee population, colony attributes J. Inst. Agric. Anim. Sci. 27:133-137 (2006)

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48

Yang, Jing Mei. "Regression & Correlation Analysis of Higher Mathematics on Probability and Statistics." Applied Mechanics and Materials 556-562 (May 2014): 4167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4167.

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Анотація:

Higher mathematics and Probability and statistics are the core course in university,There is a close relation between the two. By comparison and analysis of the scores of the two courses, this paper attempts to show that higher math scores on the impact of Probability and statistics, Higher mathematics X is in linear relationship with Probability and statistics Y,The coefficients for the two is r=0.541. the correlativity between higher mathematics and Probability and statistics has been investigated by regression analysis,the regression equation between the two is obtained,therefore the quantitative analysis can be carried out.Conclusion: There is a good linear correlation between higher mathematics scores and Probability and statistics scores.Description the quality of higher mathematics achievements on the impact of Probability and statistics can not be ignored, for the construction of Higher mathematics courses provide a strong basis.

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49

Dunlap, William P., and Ronald S. Landis. "Interpretations of Multiple Regression Borrowed From Factor Analysis and Canonical Correlation." Journal of General Psychology 125, no. 4 (October 1998): 397–407. http://dx.doi.org/10.1080/00221309809595345.

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50

KOLODII, Svitlana. "CORRELATION-REGRESSION ANALYSIS OF GDP AND AVERAGE WAGE INDICATORS IN UKRAINE." CHERKASY UNIVERSITY BULLETIN: ECONOMICS SCIENCES, no. 1 (2020): 82–89. http://dx.doi.org/10.31651/2076-5843-2020-1-82-89.

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