REFERENCES
1. Agresti, A. (2002), Categorical Data Analysis, 2nd. Ed., New York: John Wiley and Sons.
2. Akaike, H. (1973), “Information Theory and an Extension of Maximum Likelihood Principle,” in Second International Symposium on lnformation Theory (B. N. Petrov and F. Caski, Eds.) Akademia Kiado, Budapest, 267–281.
3. Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer-Verlag.
4. Anscombe, F. J. (1960), “Rejection of Outliers,” Technometrics, 2, 123–167.
5. Anscombe, F. J. (1973), “Graphs in Statistical Analysis,” The American Statistician, 27, 17–21.
6. Atkinson, A. C. (1985), Plots, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis, Oxford: Clarendon Press.
7. Barnett, V. and Lewis, T. (1994), Outliers in Statistical Data, 3rd ed., New York: John Wiley & Sons.
8. Bartlett, G., Stewart, J., and Abrahamowicz, M. (1998), “Quantitative Sensory Testing of Peripheral Nerves,” Student: A Statistical Journal for Graduate Students, 2, 289–301.
9. Bates, D. M. and Watts, D. G. (1988), Nonlinear Regression Analysis and Its Applications, New York: John Wiley & Sons.
10. Becker, R. A., Cleveland, W. S., and Wilks, A. R (1987), “Dynamic Graphics for Data Analysis,” Statistical Science, 2, 4, 355–395.
11. Belsley, D. A. (1991), Conditioning Diagnostics: Collinearity and Weak Data in Regression, New York: John Wiley & Sons.
12. Belsley, D. A., ...
Get Regression Analysis by Example, 4th Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.