Minhhoa bai giang kinh te luong
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The document reports the results of a regression analysis with LUONGHANG as the dependent variable. The regression found GIA and QUANGCAO to be statistically significant predictors of LUONGHANG, while C was also a statistically significant predictor and positively related to LUONGHANG. Additional tests found C to be statistically different from -1.
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The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the data and estimated model meet the assumptions of regression analysis. Specifically, the variables are stationary and there is no multicollinearity while the model and estimated coefficients are statistically significant with a goodness of fit of 95%. Therefore, the Cobb-Douglas production function appropriately captures the relationship between L, K, and Q in the data.Cobb-douglas production function
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The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the variables are stationary and there is no multicollinearity. The model is found to be statistically significant and a good fit to the data based on the F-statistic and R-squared value. It is determined that industry exhibits increasing returns to scale since the estimated coefficients of K and L sum to above 1.Output minitab david
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The regression analysis found that ln Y is predicted by ln X1, ln X2, ln X3, ln X4, ln X5 and ln X6, with ln X1 being the strongest predictor. However, ln X2 and ln X3 are highly correlated with other predictors. The regression explains 99.7% of the variation in ln Y. The analysis identified 6 observations as having large standardized residuals or high influence based on their X values.Chapter14
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The document contains a regression analysis of house prices using four predictor variables. It includes:
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3) Analysis of variance tables and calculations showing the regression model is significant overall.
4) Comparison of three regression models, finding the second model is superior to the first but the third is not an improvement on the second.
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- 1. Dependent Variable: LUONGHANG Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. GIA -1.331220 0.086149 -15.45251 0.0000 QUANGCAO 3.578441 1.263821 2.831445 0.0115 C 18.57448 1.327863 13.98825 0.0000 R-squared 0.939080 Mean dependent var 15.30000 Adjusted R-squared 0.931913 S.D. dependent var 2.494204 S.E. of regression 0.650825 Akaike info criterion 2.116 Sum squared resid 7.200735 Schwarz criterion 2.265688 Log likelihood -18.1632 F-statistic 131.0274 Durbin-Watson stat 2.816702 Prob(F-statistic) 0.000000
- 2. Wald Test: Equation: EQ01 Null Hypothesis: C(1)= -1 F-statistic 14.78196 Probability 0.001298 Chi-square 14.78196 Probability 0.000121