We used a v2 test (1 df) to compare observed frequencies of correct and incorrect predictions, and therefore to assess if models predict distribution significantly better than expected under random expectations (Roura-Pascual et al. 2004).
Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method
Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```We used the stepwise multiple linear regression that accounts for repeated measurements in the same dog (4) first to test whether there was a difference among values for resistances and for elastances between control and emphysema at each mean Paw, considering the entire range of f and VT of forcing.```
Sample intent: background



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
Sample intent: background



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method
Sample text:```distances were calculated between all population pairs considering the likely routes of human migration out of Africa, following the criteria set by Ramachandran et al. (2005). The test was performed with the software Passage version 2.```
Sample intent: background



Sample text:```These models are not specified and observe a single phenomenon from different points of view (Maxfield et al., 2002; Parker et al., 2009).```
Sample intent: method



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```We used the stepwise multiple linear regression that accounts for repeated measurements in the same dog (4) first to test whether there was a difference among values for resistances and for elastances between control and emphysema at each mean Paw, considering the entire range of f and VT of forcing.```
Sample intent: background



Sample text:```The lower abundance of fish at impacted reaches detected in our study agrees with the results of other authors, who in some cases found four times higher biomass at control reaches (Kube cka et al. 1997; Almod ovar & Nicola 1999).```
Sample intent: result



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```Estimation of overall mortality ratio (relative risk) was carried out using the random e ects model according to DerSimonian and Laird [9].```
Sample intent: background



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
Sample intent: background