Sentence examples for models estimates with from inspiring English sources

Our multiple logistic regression and structural equations models estimates with a statistical significance level of 1% (p < 0.01) or 5% (p < 0.05) may not need to be corrected, because the two outcome indicators and multiple explanatory factors in our case are complementary and not independent of each other.

Linear probability models estimated with the full sample are reported in Table 3.

All models estimated with a linear probability model and adjust for personal characteristics, and lagged state and year fixed effects.

The resulting path loss models estimated with the least square regression for both location clusters are shown in Figure 8.

All models estimated with OLS and adjust for personal characteristics, school-leaving state and year fixed effects.

Table 7 shows the average effects for each of the models estimated, with standard errors obtained by bootstrapping (500 replications).

All models estimated with a linear probability model and adjust for personal characteristics, and school-leaving state and year fixed effects.

However, the models estimated with the use of monthly price data series did not pro- 399 vide well specified statistical results.

The Bayesian posterior probabilities (BPP) used models estimated with Modeltest 3.7 under AIC.

Linear and inverse models were tested, and the inverse models explained more variance than the linear models, estimated with R values.

Models estimated with and without these respondents did not differ substantively and so, consistent with current practice, these respondents were retained in the main analysis [ 39].