regression analysis

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We further confirm this in unreported analysis where we calculate percentiles of vertical and horizontal distances and rerun the original OLS regression. We find that less than 3% of the sample, 62 of 2,145 observations, are influential as they are responsible for the significant estimated coefficient for Delaware*Post-1991.
The OLS regression model was significant and explained 31.8% of the variance.
From the OLS regression analysis in the second stage, farm size, credit access, and training were found to have negative impacts on efficiencies while membership of cooperatives have positive effects.
Column 1 applies pooled OLS regression to examine the determinants of cash holding.
Table 5: OLS Regression showing the impact of Currency Unions on Foreign Direct Investment inflows
In each table, we present estimates of the traditional OLS regression in column (1), the structural OLS regression excluding zero trade observations in column (2), the EK Tobit regression including zero trade observations in column (3); and the PPML regression including zero trade observations in column (4).
Econometric techniques applied included bivariate analysis (Chi-square), correlation metrics, and OLS regression analysis.
This preference for OLS regression stems from its simplicity and ease of use, computational efficiency, and straightforward interpretation (Hastie et al.
For a comparison of the effect of population and bootstrap weights in an OLS regression, we regressed the natural log of the level of liquid assets on the set of independent variables used for the logistic regression.
Using a technique known as meta-analysis, we investigate whether nonparametric techniques can outperform OLS regression for cost-estimating applications.
In this paper, OLS regression analysis and quantile regression analysis are used in the study, and their coefficients are compared.
Table 5: Prospective R-Squared Fit Statistics * DxCG Medicare DxCG Medicaid Prospective Concurrent Prospective CMS V21 without Rx without Rx with Rx 2011 costs Basic model General 0.2117 0.2228 0.2097 0.2487 Older 0.1743 0.1771 0.1732 0.2538 MH-SUD 0.1819 0.1970 0.1873 0.2408 High cost 0.0401 0.0676 0.0403 0.0949 Multimorbid 0.1527 0.1690 0.1520 0.1903 Low risk 0.0600 0.0684 0.0673 0.0470 Recalibrated with additional variables General 0.3485 0.3558 0.3485 0.3391 Older 0.3428 0.3480 0.3440 0.3405 MH-SUD 0.3190 0.3252 0.3182 0.3148 High cost 0.1423 0.1631 0.1434 0.1399 Multimorbid 0.2640 0.2757 0.2631 0.2544 Low risk 0.1321 0.1326 0.1319 0.1272 Notes: [R.sup.2] is based on square root OLS regression models.