Regression Analysis of Agricultural Intensity Data: LOGAGINT Model - Prof. Lawrence Herman, Study notes of Statistics

Download Regression Analysis of Agricultural Intensity Data: LOGAGINT Model - Prof. Lawrence Herman and more Study notes Statistics in PDF only on Docsity! Model Diagnostics Agricultural Intensity Data ANOVAb 4.000 5 .800 21.680 .000a .849 23 .037 4.848 28 Regression Residual Total Model 1 Sum of Squares df Mean Square F Sig. Predictors: (Constant), POPDRY, ALLUV, POP, POPLVSTK, DRYSSNa. Dependent Variable: LOGAGINTb. Coefficientsa .825 .072 11.388 .000 .004 .001 .652 4.552 .000 .109 .028 .620 3.951 .001 .193 .076 .225 2.549 .018 .002 .001 .364 2.333 .029 -.001 .000 -.419 -2.130 .044 (Constant) POP DRYSSN ALLUV POPLVSTK POPDRY Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: LOGAGINTa. Regression Standardized Residual 2.001.501.00.500.00-.50-1.00-1.50-2.00 Histogram Dependent Variable: LOGAGINT F re q u e n cy 8 6 4 2 0 Std. Dev = .91 Mean = 0.00 N = 29.00 POP 3002001000-100 S tu d e n tiz e d R e si d u a l 2 1 0 -1 -2 Histogram of Studentized Residuals Plot of Studentized Residuals vs POP Scatterplot Dependent Variable: LOGAGINT Regression Standardized Predicted Value 3210-1-2 R e g re ss io n S tu d e n tiz e d R e si d u a l 3 2 1 0 -1 -2 -3 Group StdRes Lev dffits dfbeta0 dfbeta1 dfbeta2 dfbeta3 dfbeta4 dfbeta5 1 -1.822 0.179 -1.004 -0.081 0.112 -0.760 0.387 -0.290 0.622 2 -1.952 0.119 -0.889 -0.396 0.315 0.435 -0.610 0.018 -0.291 3 -0.025 0.102 -0.010 -0.001 -0.001 -0.006 0.004 0.000 0.004 4 -0.290 0.106 -0.115 -0.115 0.062 0.068 0.036 0.011 -0.056 5 0.077 0.145 0.035 -0.005 0.001 0.002 0.020 0.016 -0.012 6 0.569 0.141 0.259 0.133 -0.102 -0.141 0.169 -0.026 0.109 7 -1.241 0.078 -0.447 -0.441 0.236 0.240 0.162 -0.007 -0.173 8 0.562 0.244 0.344 -0.108 0.052 0.078 0.148 0.183 -0.180 9 0.040 0.102 0.016 0.016 -0.008 -0.009 -0.005 -0.002 0.008 10 -0.112 0.089 -0.041 -0.014 0.009 -0.023 0.019 -0.008 0.018 11 -0.135 0.085 -0.049 -0.017 0.009 -0.027 0.022 -0.008 0.021 12 0.155 0.104 0.061 0.022 -0.019 -0.025 0.043 0.004 0.013 13 0.378 0.072 0.128 0.122 -0.033 -0.075 -0.044 -0.039 0.060 14 -0.439 0.256 -0.276 0.036 -0.042 -0.095 0.087 -0.177 0.178 15 -0.578 0.124 -0.247 -0.069 -0.140 0.038 0.062 0.196 -0.076 16 1.123 0.070 0.387 0.363 -0.087 -0.224 -0.132 -0.127 0.177 17 1.092 0.117 0.463 0.065 0.001 0.086 -0.185 0.285 -0.236 18 0.986 0.448 0.953 -0.065 -0.089 -0.041 0.359 -0.153 0.486 19 -0.858 0.241 -0.526 0.169 -0.125 -0.326 -0.232 0.102 0.149 20 0.894 0.649 1.308 -0.245 1.209 0.052 -0.123 -0.881 -0.125 21 0.636 0.098 0.245 0.067 -0.061 -0.083 0.175 0.038 0.026 22 -0.839 0.049 -0.251 -0.192 0.092 0.070 0.118 -0.085 0.000 23 -1.621 0.241 -1.037 0.052 -0.642 0.257 -0.591 0.701 -0.167 24 2.033 0.067 0.736 0.661 -0.090 -0.413 -0.253 -0.288 0.322 25 -0.134 0.303 -0.094 -0.016 0.011 0.015 0.020 0.004 -0.052 26 1.134 0.104 0.457 0.023 0.004 0.054 -0.181 0.191 -0.009 27 -1.500 0.351 -1.223 0.098 -0.132 0.130 0.269 -0.385 -0.203 28 0.370 0.071 0.124 0.045 -0.015 0.064 -0.059 0.008 -0.045 29 1.649 0.246 1.071 -0.311 0.061 0.788 0.429 0.129 -0.583 Studentized Residuals versus Standardized Predicted Values