Advanced Regression Models: Least Squares, Nonlinear, Poisson and Binary Logistics Regression Using R
Analysis in data science and digital business requires analysis of the data and in many cases the use of regression techniques.
References and Suggested Reading
- 1.Affi, A., & Azen, S. (1979). Statistical analysis (2nd ed., pp. 143–144). London, UK: Academic Press.Google Scholar
- 2.Devore, J. (2012). Probability and statistics for engineering and the sciences (8th ed., pp. 211–217). Belmont, CA: Cengage Publisher. Google Scholar
- 4.Fox, W. (2012). Mathematical modeling with maple. Boston, MA: Cengage Publishers.Google Scholar
- 5.Fox, W. P. (2011, October–December). Using the EXCEL solver for nonlinear regression. Computers in Education Journal (COED), 2(4), 77–86.Google Scholar
- 6.Fox, W. P. (2012). Issues and importance of “good” starting points for nonlinear regression for mathematical modeling with maple: Basic model fitting to make predictions with oscillating data. Journal of Computers in Mathematics and Science Teaching., 31(1), 1–16.Google Scholar
- 7.Giordano, F., Fox, W., & Horton, S. (2013). A first course in mathematical modeling (5th ed.). Boston, MA: Cengage Publishers.Google Scholar
- 8.Johnson, I. (2012). An introductory handbook on probability, statistics, and excel. Retrieved July 11, 2012, from http://records.viu.ca/~johnstoi/maybe/maybe4.htm.
- 10.Melander, E., Oberg, M. & Hall, J. (2006). The ‘New Wars’ debate revisited: An empirical evaluation of the atrociousness of ‘New Wars’. Uppsala peace research papers no. 9, department of peace and conflict research. Sweden: Uppsala University. http://www.musik.uu.se/digitalAssets/18/18585_UPRP_No_9.pdf. Accessed 12 Sept 2012.
- 11.Neter, J., Kutner, M., Nachtsheim, C., & Wasserman, W. (1996). Applied linear statistical models (4th ed., pp. 531–547). Chicago, Il: Irwin Press.Google Scholar
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