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University-industry cooperation is the formation of partnership relationships that exist in Science and Technology Institutions with industries, that is, it is the cooperation that exists between universities and industries. In this sense, there are several ways of forming relations between universities and industry, and for this to happen, it all boils down to cooperation, since both agents need to agree with certain achievements, there must be communication between them. Despite all the existing benefits, even if there is reciprocity between these agents that seek a common denominator, there are still divergences that remain as a difficulty factor for this cooperation, since there are several differences found in the academic and industrial environment. Thus, we sought to analyze how are the production of articles aimed at university-industry cooperation, as well as patents related to this subject, through specific bases. A forecast analysis was also carried out using the Poisson Regression models, where it was found, in the data of patents and articles, a superdispersion, therefore, it was necessary to adjust the deviation G ^ 2 or as known deviance, and with the adjustment of overdispersion the models were adequate and confirmed in the forecast made. We thank Capes and CNPq for their support and financial support.
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ALVARENGA, A. M. T. Generalized linear models: application to road accident data. 2015. Doctoral thesis.
 Ankrah, S.; Al-Tabaa, O. Universities-industry collaboration: a systematic review. Scandinavian Journal of Management, v. 31 pp, 387-408, 2015.
 Agustinho, E. O .; Garcia, E. N. Innovation, Technology Transfer and Cooperation. Law and Development, v. 9, n. 1, p. 223-239, 2018.
 Bercovitz, J., Feldman, M., 2006. Entrepreneurial universities and technology transfer: a conceptual framework for understanding knowledge-based economic development. Journal of Technology Transfer 31 (1), 175–188.
 Boente, A.; BRAGA, G. Contemporary scientific methodology for university students and researchers. Rio de Janeiro: Brasport, 2004.
 Bolfarine, H.; Sandoval, M. Introduction to Statistical Inference. Brazilian Mathematical Society, 2001.
 Bockenholt, U. Mixed INAR (1) Poisson regression models: analyzing heterogeneity and serial dependencies in longitudinal count data. Journal of Econometrics. v.89 pp.317-338. 1999.
 Company-university COOPERATION in Brazil: a new prospective balance. In A. G. Plonski (Coord.). University-business interaction (Vol. 1, pp. 09-23). Brasília: IBICT, 1998.
 Cordeiro, G. Introduction to the likelihood theory. Textbook of the 10th National Symposium on Probability and Statistics. UFRJ / ABE. Rio de Janeiro. 1992.
 Cordeiro, G. M.; Demétrio, C. G.B. Generalized linear models and extensions. São Paulo, 2008.
 Dias, Alexandre Aparecido; Porto, Geciane Silva. How does USP transfer technology ?. Organ. Soc., Salvador, v. 21, n. 70, p. 489-507, Sept. 2014.
 Ender, P. Applied categorical & nonnormal data analysis: Poisson models. UCLA California Class Notes. 2002. http://www.gseis.ucla.edu/courses/ed231c/notes1/pois1.html.
 Etzkowitz, H.; Leydesdorf, L. The dynamics of innovation: from National Systems and "Mode 2" to a Triple Helix of university-industry-government relations. Research Policy, n. 29, 2004.
 Etzkowitz, H., Ledesdorff, L. Introduction: universities in the global knowledge economy. In H. Etzkowitz, & L. Leydesdorff (Eds.). Universities and the global knowledge economy: a triple helix of university-industry-government relations (pp. 1-10). Londres: Continuum, 1997.
 Garnica, Leonardo Augusto et al. Technology transfer and intellectual property management in public universities in the State of São Paulo. Dissertation (Master in PRODUCTION ENGINEERING - Postgraduate Program in Production Engineering at the Federal University of São Carlos, São Carlos, 2007.
 Gusmão, R. International science-industry collaboration practices and policies. Brazilian Journal of Innovation, v. 1, n. 2, p. 327-360, 2002.
Kroll, M. Nonparametric Poisson Regression from Independent and Weakly Dependent Observations by Model Selection. Nonparametric Adaptive Poisson Regression. Universitat Mannheim, 2018.
 Mansfield, E., 1995. Academic research underlying industrial innovations: sources,
characteristics, and financing. Review of Economics and Statistics 77 (1), 55–65.
 Nelder, J. A.; Wdderburn, R. W. M. Generalized linear models. Journal of Royal Statistical Society: v135 pp 370-384. 1972.
 Paula, G. A. Estimation and tests in regression models with restricted parameters. Textbook of the 5th School of Regression Models. IME-USP / ABE. Campos do Jordão. 1997.
 Perkmann, M.; King, Z.; Pavelin, S. Engaging excellence? Effects of faculty quality on university engagement with industry. Research Policy, v. 40, p. 539-552, 2011.
 Richardson, R.J. Social research: methods and techniques. 3rd ed. São Paulo: Atlas, 1999.
 Rosa, R. A .; Paloma, A. R. Pinheiro Junior, L. P .; FREGA, J. R. University-Company Cooperation: a bibliometric and sociometric study in Brazilian scientific management journals. UNIMEP Management Magazine, v.16, n.1, 2018.
 Russo, S. L.; Fabris, J. P. ; Zayas-Castro, J. ; Camargo, M. E. . Linking Past and Future Research about University-Industry Cooperation: a Systematic Review. International Business Management, v. 11, p. 1753-1993, 2017.
 Russo, S. L. Control charts for self-correlated non-conforming variables. Florianópilis, 2002. 120 f. Thesis (PhD in Production Engineering) - Federal University of Santa Catarina.
 Russo, L. S .; CAMARGO, M.E .; SAMOHYL, R.W. Control graphs based on the residuals of the POISSON * regression model. Online Production Magazine. Vol.8 n.4 Dec.2008 Available at http://producaoonline.org.br/index.php/rpo/article/viewFile/138/212.
 Santana, Élcio Eduardo de Paula; Porto, Geciane Silveira. Now, what to do with this technology? A Multi-Case Study on Technology Transfer Possibilities at USP-RP / Gee, What Should I Do with This Tecnology? A Multicase Study about the Possibilities of Technology Transfer at USP-RP. Contemporary Administration Magazine, v. 13, n. 3, p. 410, 2009.
 Santos, E. F .; Benneworth, P. University-Company Interaction: characteristics identified in the literature and the regional collaboration of the University of Twente. RASI, v. 5, n. 2, pp. 115-143, 2019.
 Santos, M. E. R .; Toledo, P. T. M .; Lotufo, R. A. Technology Transfer: Strategies for structuring and managing Technological Innovation Centers. In: TORKOMIAN, A. L. V. (Org.). Panorama of the Technological Innovation Centers in Brazil Campinas, SP: Ed. Komedi, p. 19-38, 2009.
Sharfer, J. L. Analysis of incomplete multivariate data. London Chapman & Hall. 1997.
 Takahashi, A.; Kurosawa, T. Regression correlation coefficient for a Poisson regression model. Computational Statistics & Data Analysis, v. 98, p. 71-78, 2016.
 Takahashi, V. P Transfer of technological knowledge: a multiple case study in the pharmaceutical industry, Gestão & Produção, v. 12, n. 2, p. 255-269, 2005.
 Tadano, Y. S., UGAYA, C. M., FRANCO, A. T., Poisson regression method: methodology for assessing the impact of air pollution on population health, Ambiente & Sociedade, 2009.
 Tatum, C. T.; Conceição, F. F.; Tatum, L. M. M. Fabris, J. P.; Russo, S. L. University-Industry Cooperation Network in Academic and Technological Productivity. Revista GEINTEC: gestão, inovação e tecnologias, v. 8, p. 4697-4709, 2018.
 Vergara, Sylvia Constant. Projects and research reports in administration. 8. Ed. São Paulo: Atlas, 2007.
 Zeger, S. L.; Liang, K -Y. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. v. 42 pp.121-130. 1986.
 Zeviani, W. M., Júnior, E. R., Taconeli, C. A. Regression Models for Count Data with R. Laboratory of Statistics and Geoinformation Department of Statistics Federal University of Paraná, 2016.