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...38814 x_5 3.76952 0.67006 5.626 1.87e-08 *** x_6 0.07698 0.01565 4.919 8.75e-07 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Residual standard error: 33.76 on 19710 degrees of freedom Multiple R-squared: 0.006298, Adjusted R-squared: 0.005995 F-statistic: 20.82 on 6 and 19710 DF, p-value: < 2.2e-16 I have certain questions with this model 1. Any way to improve the accuracy of this model? 2.Which of the value is most useful among Residual standard error,degrees of freedom, Multiple R-squared, Adjusted R-squared, F-statisti, p-value for choos...

Dear all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains 4335 observations. I'm quite new to R and would like to ask if someone has implemented the method proposed by Su and Yang in R or if anyone...

Hello, I'm new in R. I'm meteorological modeller and i will calculate some statistics for my model results. These statistis are the follow: ANB: Average Normalized Absolute BIAS MNB: Mean Normalized BIAS MNE: Mean Normalised Error STDE: Standard Deviation of Error FB: Fractional BIAS MG: Geometric Mean BIAS VG: Geometric Variance SKVAR: Skill Variance RMSE: Root Mean Square Erro...

Dear all, I'm performing RDA on plant presence/absence data, constrained by geographical locations. I'd like to constrain the RDA by the "extended matrix of geographical coordinates" -ie the matrix of geographical coordinates completed by adding all terms of a cubic trend surface regression- . This is the command I use (package vegan): >rda(Helling ~

...ooks like this Y = B + B1*D1 + B2*X + B3*(X*D1) Where Y = Daily Cash market price D1 = Dummy variable taking value 1 for period Oct-Mar and 0 for Apr-Sep X = Daily futures market price on which cross hedging is done. B,B1,B2,B3 are the slope co-efficients. The results look like this Regression Statistics Multiple R 0.948702709 R Square 0.900036831 Adjusted R Square 0.89981135 Standard Error 25.52050965 Observations 1334 Coefficients Standard Error t Stat P-value Intercept 53.817 4.375 12.300 0.000 X 0.986 0.012 80.283 0.000 D1 27.399 6.106 4.487 0.000 D1 * X -0.100 0.017 -5.820 0.0...