By OECD OCDE
This annual variation of Labour strength data offers targeted information on inhabitants, labour strength, employment and unemployment, damaged down by way of gender, in addition to unemployment period, employment prestige, employment by way of region of task and part-time employment. It additionally comprises participation and unemployment premiums by way of gender and exact age teams in addition to comparative tables for the most parts of the labour strength. info can be found for every OECD Member state and for OECD-Total, Euro zone and ecu Union. The time sequence offered within the book conceal two decades for many international locations. It additionally offers info at the resources and definitions utilized by Member nations within the compilation of these data.
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Additional resources for Labour Force Statistics 2010 - Statistiques de la population active 2010
276) at the sample mean. 38 7. 276. 964. Add the logistic regression coefficient to this logit and then find the probability. 040). 007). 8. Compute standardized coefficients for the independent variables. SPSS saves the predicted probabilities from logistic regression. 1018. 6195. 467. 977. 2 makes the calculations. One method gives larger standardized coefficients than the other method, but both indicate that education and age have the strongest influence on smoking. 024 39 Summary Logistic regression coefficients provide a simple linear and additive summary of the influence of a variable on the logged odds of having a characteristic or experiencing an event, but lack an intuitively meaningful scale of interpretation of change in the dependent variable.
Applied to individual logistic regression coefficients, the BIC differs from p values. Based on some complex derivations and approximations, Raftery (1995, p. 139) suggests that, to reject the null hypothesis, the squared t or, in this case, the squared z and chi-square value for a coefficient should exceed the logarithm of the sample size. In terms of formula, the BIC value, BIC = z2 -Inn, should exceed zero to reach significance. Specifically, the BIC value refers to the difference in model information with and without the variable and coefficient in question.
83. As discussed earlier, besides using the mean of the dependent variable as a starting value, one can use the predicted probability based on the means of all continuous variables and the modal categories of the dummy variables. As also discussed earlier, one could even deal with the nonlinearity of effects on probabilities by calculating a set of effects on probabilities defined by various values of the independent variables. 9 The variety of ways to interpret the effects on probabilities indicates the difficulty in summarizing nonlinear relationships.
Labour Force Statistics 2010 - Statistiques de la population active 2010 by OECD OCDE