regression

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Estimates of variances (diagonal) and (co)variances (below diagonal) between the random regression coefficients (1) with standard deviations in parentheses and eigenvalues ([lambda]) of (co)variances matrix of the additive genetic effects and permanent environmental effects with percentage of variance explained in brackets in Leg3,5 model (2) Coefficients of additive genetic effect [[alpha].
As shown in Table 2, while standard errors varied by just 16% across the models, a Type II error was an artifact of the wide variation in regression coefficients (63% difference) between the weighted and unweighted models.
The remaining regression coefficients for the generalized equipment maps are shown in Table 2 and Table 3.
When lifetime residence on a farm is included in the statistical models (Table 4), it is a stronger predictor of height and weight than the exposure indices as indicated by the lower p-values of the regression coefficients, thus providing further indications that farm residence is the factor determining environmental exposure.
Regression coefficient is significant at the 10% (2-tailed), ** Regression coefficient is significant at the 5% (2-tailed), *** Regression coefficient is significant at the 1% (2-tailed).
Table 6 shows the regression coefficients for the difference specification of Okun's law for the labor force aged 20 years and older.
In response to Hypothesis II, the regression coefficient is used to test whether the China's OFDI flow amount will increase as the amount of natural gas supply of an African recipient country increases.
The regression coefficient and odds ratio for the profession of the husbands were -.
The most influence of the nullity of preassumptions is the biased variance estimation, regression coefficients and coefficient of determination, also is the biased tests hypothesis, standard error of regression coefficient and interval estimation.
The value of the regression coefficient b = 0,0274 shows by how much the resulting variable Y is modified, in case that the factorial characteristic is modified by 1 unit; thus, by increasing the investment volume in year t-1 by 1 million lei, the existing accommodation capacity will increase in the next year (t) by 0.
This difference can be seen in the positive value of the regression coefficient (GPA*On-Off).