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<p style="text-align:justify"><span style="font-size:16px">In regression analysis while estimating the parameters our data should follow the assumption of classical regression to have the valid estimator of the regression parameters. Sometime while estimating the parameter data does not follow the assumption of classical regression like constant variance of residual and autocorrelation of residual in that situation classical approach of estimating the regression parameter ordinary least square will not give valid estimators in that situation generalized least square will used.</span></p>
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<p style="text-align:justify"><span style="font-size:16px"><strong>Vaibhav Chittora</strong></span></p> <p style="text-align:justify"><span style="font-size:16px"><em>Dr. Y.S. Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh (173 230), India</em></span></p>
How to cite
<p><span style="font-size:14px">Chittora, V., 2022. Generalized least square method for regression analysis. <em>Biotica Research Today</em> 4(2), 132-134.</span></p>
Reference
<p style="text-align:justify"><span style="font-size:16px">Greene, W.H., 2003.<em> Econometric Analysis. </em>Prentice Hall, New Jersey, p. 828.</span></p> <p style="text-align:justify"><span style="font-size:16px">Gujarati, D.N., 2005. <em>Basic Econometrics</em>. McGraw-Hill, New York, p. 947.</span></p> <p style="text-align:justify"><span style="font-size:16px">Montgomery, D.C., Peck, E., Vining, G.G., 2014. <em>Introduction to Linear Regression Analysis. </em>WILEY Singapore, p. 672.</span></p> <p style="text-align:justify"><span style="font-size:16px">Musau, M.V., Waititu, A.G.P., Wanjoya<em>, </em>A.K., 2015. Modelling Panel Data: Comparison of GLS Estimation and Robust Covariance Matrix Estimation. <em><span style="background-color:white">American Journal of Theoretical and Applied Statistics</span></em><em> </em>4(3),<em> </em>185-191.</span></p>
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