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TÜRKİYEDE HAYVANCILIK SEKTÖRÜNDE LOJİSTİK REGRESYON UYGULAMALARININ ÖNEMİ LOJİSTİK REGRESYON UYGULAMALARI/ALANLARI

Year 2012, Volume: 16 Issue: 2, 25 - 36, 20.02.2014

Abstract

Lojistik regresyon analizi ikili bağımlı değişkenleri modellemek için uygulanabilen en çok tercih edilen regresyon metotlarından biridir. Lojistik regresyon X1, X2, …, Xn gibi bağımsız
değişkenleri ile iki olası kategori için O veya 1 gibi kodlanmış Y ikili bağımlı değişkeni arasındaki ilişkiyi tanımlamak için kullanılan matematiksel modelleme yaklaşımıdır. Burada bağımsız değişkenler sürekli, kesikli, ikili veya bunların karışımı olabilir. Bu çalışmada lojistik regresyon modelleri araştırmaktadır. En çok olabilirlik metotları lojistik modelin parametrelerini tahmin etmek için kullanılır. Katsayıların yorumu odds oran değerleriyle yapılır. Bir başka deyişle bu araştırmada, ikili sonuç değişkeni ile hem sürekli hem de kesikli değişkenlerden oluşan bağımsız değişkenler kümesi arasındaki ilişkiyi tanımlayabilen lojistik regresyon analizi incelenmiştir. Kısaca, bu çalışmada lojistik regresyonun hayvancılıkta uygulanabilirliği ele alınmıştır.

References

  • Akgül A. and Çevik O., 2003, Statistical
  • Analysis Methods “Management
  • Implementation is SPSS”, Emek Offset,
  • Ankara.
  • Alpar, R., Regression Overview Presentation,
  • http://www.biyoistatistik.hacettepe.edu.tr/D
  • onem_III/Turkce/coklu_dogrusalolmayan_
  • lojistik.pps#364,30, Slide 30
  • Atakurt, Y., 1999, Logistic Regression Analysis
  • and an Implementation in Its Use in
  • Medicine, Ankara University Faculty of
  • Medicine Journal, C.52, Issue 4, P.195,
  • Ankara
  • Başarır, G.,1990, Discrimination Issue in
  • Multivariable Data and Logistic Regression
  • Analysis (Applied statistics doctoral thesis)
  • Hacettepe. U., 1-36, Ankara
  • -
  • J.Agric. Fac. HR.U., 2012, 16(2) Korkmaz et al.
  • Bircan H., Logistic Regression Analysis: Practice in Medical Data, Kocaeli University Social Sciences Institute Journal, 2004 / 2: 185-208
  • Bonney, G. E., 1987, Logistic Regression for dependent binary observations. Biometrics, 43(4): 951-973.
  • Buescher, P.A., Larson, L.C., Nelson, M.D., Lenihan, A.J., 1993, Prenatal WIC Participation Can Reduce Low Birth Weight and Newborn Medical Costs: A Cost Benefit Analysis of Wic Participation in North Carolina, Journal of the American Dietetic Association, 93:163-166.
  • BUİS, 2005
  • Costanza M.E., Staddat A.M., Gaw V. and Zaplea J.G., 1992, “The Risk Factors of Age and Family History and Relationship To Screening Mammography Utilization”, Journal of The American Statistical Association, 40, 776.
  • Çolak, E., Özdamar K., 2004, Review of Conditional and Limited Regression Models by the Risk Factors in Fatal Traffic Accidents, OGÜ Faculty of Medicine Journal, Volume 26 P.1 Eskişehir
  • Elhan, A.H, 1997, Review of Logistic Regression Analysis and Implementation in Medicine. (PhD thesis in biostatistics) A.U., 4-29, Ankara
  • Field, A., 2000, Discovering Statistics, Sage Publications
  • Gardside, P.S., Glueck, C.J., 1995, The Important Role of Modifiable Dietary and Behaviour Characteristic in the Causation and Prevention of Coronary Heart Disease Hospitalization and Mortality. Journal of American College of Nutrition, 14: 71-79.
  • Girginer, N, 2008, Measuring the Satisfaction of Tramway Passengers with Logistic Regression Analysis: Estram Pattern, Celal Bayar University FEAC Management and Economics Journal, Manisa
  • Gujurati, D. N., 1995, “Basic Econometrics”, McGraw-Hill, Inc., New York
  • Kloiber, L.L., Winn, N.J., Shaffer, S.G., Hassanein, R.S., 1996, Late Hyponatremia in very Low Birth Weight Infants: Incidence and Associated Risk Factors. Journal of the American Dietetic Association, 96: 880-884.
  • Kurtuluş, K., 1985, Marketing Research, Economics and Management Institute, 3. Edition
  • Lee K. and Koval J.J., 1997, “Determination of The Best Significance Level in Forward Stepwise Logistic Regression”, Communication in Statistics, 26(B), 566.
  • Peoples, M.D., Siegel, E., Suchi-ndran, C.M., Origasa, H., Ware, A., Barakat, A., 1991, Characteristics of Maternal Employment during Pregnancy: Effects on Low Birth weight. American Journal of Public Health. 81: 1007-1012.
  • Santos, I.S., Victoria, C.G., Huttly, S., Carvalhal, J.B., 1998, Caffeine Intake and Low Birth Weight: A Population Based Case Control Study. American Journal of M., 1988, The Retreat From Class: A New True Socialism, London: Verso.
  • Seven, Z., 1997, Comparing Stepwise Variable Selection and Stepwise Discriminant Analysis as Variable Selection Method, PhD thesis, Ankara
  • Şahin M., 1999, Logistic Regression and Its Use in Biological Fields, Kahramanmaraş,
  • Özdamar K., 2002, Statistical Data Analysis Using Package Programs–I, 4. Edition, Kaan Bookstore, Eskişehir
  • ÜNAL, 1996
  • Wang, P., Putterman, M. L., 1998, Mixed logistic regression models. Journal of Agriculture, Biological and Environmental Statistics, 3(2): 175-200.
  • http://epidemiyoloji.org/moodle/mod/wiki/view.php?id=741&page=Lojistik+regresyon&MoodleSession=16b88071cfe0a1c9581788013d2eb068
  • http://www.deu.edu.tr/userweb/k.yaralioglu/dosyalar/ver_mad.doc
  • http://www.sayisalyontemler.com/?q=content/cok-kategorili-lojistik-regresyon-analizi
  • http://fikretgultekin.com/yukseklisans/Regresyon%20Analizi.pdf
  • http://www.yildiz.edu.tr/~tastan/teachi-ng/tahminyont_slides.pdf
  • http://oak.cats.ohiou.edu/~milesd/logistic.ppt#283,1,Alternative Methods of Regression
  • http://en.wikipedia.org/wiki/Logistic_regression
  • -

THE IMPORTANCE OF LOGISTIC REGRESSION IMPLEMENTATIONS IN THE TURKISH LIVESTOCK SECTOR AND LOGISTIC REGRESSION IMPLEMENTATIONS/FIELDS

Year 2012, Volume: 16 Issue: 2, 25 - 36, 20.02.2014

Abstract

Logistic regression analysis is one of the mostly preferred regression methods that can be implemented in modelling binary dependent variables. Logistic regression is a mathematical
modelling approach used to define the relationship between such independent variables as X1, X2, …, Xn and Y binary dependent variable which is coded as 0 or 1 for two possible categories. The independent variables may be continuous, discrete, binary or a combination of them. In this paper, logistic regression models are researched. Maximum likelihood methods may be used to estimate the parameters of the logistic model. The interpretations of coefficients are made with odds rate values. In other words, in this paper, the logistic regression analysis has been reviewed that can define the relationship between the binary result variable and independent variables comprising of both continuous and discrete variables. Shortly, the applicability of logistic regression in the livestock has been researched. 

References

  • Akgül A. and Çevik O., 2003, Statistical
  • Analysis Methods “Management
  • Implementation is SPSS”, Emek Offset,
  • Ankara.
  • Alpar, R., Regression Overview Presentation,
  • http://www.biyoistatistik.hacettepe.edu.tr/D
  • onem_III/Turkce/coklu_dogrusalolmayan_
  • lojistik.pps#364,30, Slide 30
  • Atakurt, Y., 1999, Logistic Regression Analysis
  • and an Implementation in Its Use in
  • Medicine, Ankara University Faculty of
  • Medicine Journal, C.52, Issue 4, P.195,
  • Ankara
  • Başarır, G.,1990, Discrimination Issue in
  • Multivariable Data and Logistic Regression
  • Analysis (Applied statistics doctoral thesis)
  • Hacettepe. U., 1-36, Ankara
  • -
  • J.Agric. Fac. HR.U., 2012, 16(2) Korkmaz et al.
  • Bircan H., Logistic Regression Analysis: Practice in Medical Data, Kocaeli University Social Sciences Institute Journal, 2004 / 2: 185-208
  • Bonney, G. E., 1987, Logistic Regression for dependent binary observations. Biometrics, 43(4): 951-973.
  • Buescher, P.A., Larson, L.C., Nelson, M.D., Lenihan, A.J., 1993, Prenatal WIC Participation Can Reduce Low Birth Weight and Newborn Medical Costs: A Cost Benefit Analysis of Wic Participation in North Carolina, Journal of the American Dietetic Association, 93:163-166.
  • BUİS, 2005
  • Costanza M.E., Staddat A.M., Gaw V. and Zaplea J.G., 1992, “The Risk Factors of Age and Family History and Relationship To Screening Mammography Utilization”, Journal of The American Statistical Association, 40, 776.
  • Çolak, E., Özdamar K., 2004, Review of Conditional and Limited Regression Models by the Risk Factors in Fatal Traffic Accidents, OGÜ Faculty of Medicine Journal, Volume 26 P.1 Eskişehir
  • Elhan, A.H, 1997, Review of Logistic Regression Analysis and Implementation in Medicine. (PhD thesis in biostatistics) A.U., 4-29, Ankara
  • Field, A., 2000, Discovering Statistics, Sage Publications
  • Gardside, P.S., Glueck, C.J., 1995, The Important Role of Modifiable Dietary and Behaviour Characteristic in the Causation and Prevention of Coronary Heart Disease Hospitalization and Mortality. Journal of American College of Nutrition, 14: 71-79.
  • Girginer, N, 2008, Measuring the Satisfaction of Tramway Passengers with Logistic Regression Analysis: Estram Pattern, Celal Bayar University FEAC Management and Economics Journal, Manisa
  • Gujurati, D. N., 1995, “Basic Econometrics”, McGraw-Hill, Inc., New York
  • Kloiber, L.L., Winn, N.J., Shaffer, S.G., Hassanein, R.S., 1996, Late Hyponatremia in very Low Birth Weight Infants: Incidence and Associated Risk Factors. Journal of the American Dietetic Association, 96: 880-884.
  • Kurtuluş, K., 1985, Marketing Research, Economics and Management Institute, 3. Edition
  • Lee K. and Koval J.J., 1997, “Determination of The Best Significance Level in Forward Stepwise Logistic Regression”, Communication in Statistics, 26(B), 566.
  • Peoples, M.D., Siegel, E., Suchi-ndran, C.M., Origasa, H., Ware, A., Barakat, A., 1991, Characteristics of Maternal Employment during Pregnancy: Effects on Low Birth weight. American Journal of Public Health. 81: 1007-1012.
  • Santos, I.S., Victoria, C.G., Huttly, S., Carvalhal, J.B., 1998, Caffeine Intake and Low Birth Weight: A Population Based Case Control Study. American Journal of M., 1988, The Retreat From Class: A New True Socialism, London: Verso.
  • Seven, Z., 1997, Comparing Stepwise Variable Selection and Stepwise Discriminant Analysis as Variable Selection Method, PhD thesis, Ankara
  • Şahin M., 1999, Logistic Regression and Its Use in Biological Fields, Kahramanmaraş,
  • Özdamar K., 2002, Statistical Data Analysis Using Package Programs–I, 4. Edition, Kaan Bookstore, Eskişehir
  • ÜNAL, 1996
  • Wang, P., Putterman, M. L., 1998, Mixed logistic regression models. Journal of Agriculture, Biological and Environmental Statistics, 3(2): 175-200.
  • http://epidemiyoloji.org/moodle/mod/wiki/view.php?id=741&page=Lojistik+regresyon&MoodleSession=16b88071cfe0a1c9581788013d2eb068
  • http://www.deu.edu.tr/userweb/k.yaralioglu/dosyalar/ver_mad.doc
  • http://www.sayisalyontemler.com/?q=content/cok-kategorili-lojistik-regresyon-analizi
  • http://fikretgultekin.com/yukseklisans/Regresyon%20Analizi.pdf
  • http://www.yildiz.edu.tr/~tastan/teachi-ng/tahminyont_slides.pdf
  • http://oak.cats.ohiou.edu/~milesd/logistic.ppt#283,1,Alternative Methods of Regression
  • http://en.wikipedia.org/wiki/Logistic_regression
  • -
There are 48 citations in total.

Details

Primary Language English
Subjects Food Engineering
Journal Section Araştırma Makaleleri
Authors

Murat Korkmaz This is me

Selami Güney

Şule Yiğiter This is me

Publication Date February 20, 2014
Submission Date February 20, 2014
Published in Issue Year 2012 Volume: 16 Issue: 2

Cite

APA Korkmaz, M., Güney, S., & Yiğiter, Ş. (2014). THE IMPORTANCE OF LOGISTIC REGRESSION IMPLEMENTATIONS IN THE TURKISH LIVESTOCK SECTOR AND LOGISTIC REGRESSION IMPLEMENTATIONS/FIELDS. Harran Tarım Ve Gıda Bilimleri Dergisi, 16(2), 25-36.

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10749  Harran Journal of Agricultural and Food Science is licensed under Creative Commons 4.0 International License.