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• Narula, S. C., “Optimization Techniques in Linear Regression. A Review”, TIMSS / Studies in Management Sciences, 19: 11-29 (1982)
• Arthanari, T. S. and Dodge, Y., “Mathematical Programming in Statistic”, John and Sons, New York, 304, 289- (1981)
• Kligman, D. and Mote, J., “Generalized Network Approaches for Solving Least Absolute Value and Tchebycheef Regression Problems”, TIMSS/Studies in Management Sciences, 19: 53- (1982)
• Lee, C. K. and Ord, J. K., “Discriminate Analysis Using Least Absolute Deviations”, Decisions Sciences, 21: 86-96 (1990)
• Narula, S. C. and Korhenen, P. J. “Multivariate Multiple Linear Regression Based on Minimum Sum of Absolute Error Criterion”, European Journal of Operation Research, 73(24): 70-75 (1994)
• Eminkahyagil, G. and Apaydın, A., “The Goal Programming in Regression Analysis”, Bulletion of The International Statistical Institute, İstanbul, (2): 137-139 (1997)
• Apaydın, A., “A Brunch, Boundary Algorithm In The Selection Of The Best Subset In Multiple Linear Regression”, Hacettepe Bulletin Of Natural Sciences and Engineering, 18: 175-192 (1997)
• Dielman, T. and Pfaffenberger, R., “LAV (Least Absolute Value) Estimation in Linear Regression, A. Review”, TIMSS/Studies in Management Sciences, 19: 31-52 (1982)
• Eminkahyagil, G., “Goal Programming and a Application, Bilim Uzmanlığı Tezi, Ankara Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 34- (1997)
• Charnes, A. Cooper, W. W., and Sueyoshi, T., “Least Squares/Ridge Regression and Goal Programming/Constrained Regression Alternatives”, European Journal of Operation Research, 27: 146-157 (1986)
• Montgomery, D. C., “Design and Analysis of 9.7 Y. j Experiments”, 4. Printed, John Wiley & Sons, New York, 171-194 (1997)
• Montgomery, D. C., “Design and Analysis of Experiments”, 2. Printed, John Wiley & Sons, New York, 165-181 (1984)
• Hosmand, A. Reaz., “Statistical Methods for Agricultural Sciences”, Timber Press, USA , 225- (1998)
• Ott, L., “An Introduction to Statistical Methods and Data Analysis, PWS Pub. Co, USA, 656-659 (1988) APPENDIX
• A-) Problems used to estimate completed random block design parameters. Problem 1 (Artificial Data) Treatment 2 3 4 05 0.03 0.06 0.01 i 15 09 0 01 0.05 0 0.04 0.1 06 0.01 0.04 0 12 0.13 0.13 0.07 11 45 Y. j 0.06 065 07 26 07 0.075 0.07 0.065 0.28 067 0.072 0.069 0.060 0.268 197 0.212 0.204 0.195 0.808 j Problem 3 (Artificial Data) Treatment 2 3 4 i 21 19 20 78 17 19 19 71 23 20 24 86 61 58 63 235 j Problem 4 (Montgomery 1997) Treatment 2 3 4 4 i 3 9.3 6 4 9.3 9.8 9.9 38.4 2 9.4 9.5 9.7 37.8 6 2 5 6 37.7 38.9 39.8 154
• Problem 5 (Artificial Data) Treatment 2 3 4 4 5 9.2 3 9.2 Y 9.2 3 4 5 37 5 8 37.2 37 37.1 148.1 j B-) Problems used to estimate balanced-incomplete random block design estimated parameters. Problem 1 (Artificial Data) Treatment 2 3 4 8 5 i 10.65 6 25 3 Y. j 5 25 3.55 5 3 8 55 10.55 11.3 11.4 Problem 2 (Artificial Data) Treatment 2 3 4 Y.ii 7.6 3 3 3.5 Y 6 2 4 8 5 10 4.4 4.8 29.7 7 j Problem 3 (Montgomery 1997) Treatment 2 3 4 75 68 - 72 75 222 224 207 218 i 218 214 Y. j
• Problem 3 (Artificial Data) Treatment 2 3 4 4 5 9.2 3 6+y 9.2 Y 9.2 3 4 y 5 8 37.2 37 27.6+y 148.1 j 4 1 1 0 Y. j 5 4 4 8 2 1 y-2 -13 y-12 Problem 5 (Montgomery 1997) Treatment 2 3 4 1 2 1 5 2 Y 5 3 y 2 j
• D-) Problems used to estimate random block design parameters in the case of two missing observations. Problem 1 (Montgomery 1997) Treat ment i 9.3 9.4 9.6 y 3 y1 + 9.4 9.3 9.8 9.9 9.2 9.4 9.5 9.7 9.7 9.6 10 y 4 8 Y. j 6 37.7 38.9 Block 2 3 4 Y.ii 0.05 0.03 0.06 0 0.04 0.03 0.01 y 01 0.15 02 0.09 0.04 0.05 y + y2 01 0.04 0 Y. j + 06 y2 08+ y1 13 0.07 0.34 y +