992 resultados para lower estimate
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
Helping behavior is any intentional behavior that benefits another living being or group (Hogg & Vaughan, 2010). People tend to underestimate the probability that others will comply with their direct requests for help (Flynn & Lake, 2008). This implies that when they need help, they will assess the probability of getting it (De Paulo, 1982, cited in Flynn & Lake, 2008) and then they will tend to estimate one that is actually lower than the real chance, so they may not even consider worth asking for it. Existing explanations for this phenomenon attribute it to a mistaken cost computation by the help seeker, who will emphasize the instrumental cost of “saying yes”, ignoring that the potential helper also needs to take into account the social cost of saying “no”. And the truth is that, especially in face-to-face interactions, the discomfort caused by refusing to help can be very high. In short, help seekers tend to fail to realize that it might be more costly to refuse to comply with a help request rather than accepting. A similar effect has been observed when estimating trustworthiness of people. Fetchenhauer and Dunning (2010) showed that people also tend to underestimate it. This bias is reduced when, instead of asymmetric feedback (getting feedback only when deciding to trust the other person), symmetric feedback (always given) was provided. This cause could as well be applicable to help seeking as people only receive feedback when they actually make their request but not otherwise. Fazio, Shook, and Eiser (2004) studied something that could be reinforcing these outcomes: Learning asymmetries. By means of a computer game called BeanFest, they showed that people learn better about negatively valenced objects (beans in this case) than about positively valenced ones. This learning asymmetry esteemed from “information gain being contingent on approach behavior” (p. 293), which could be identified with what Fetchenhauer and Dunning mention as ‘asymmetric feedback’, and hence also with help requests. Fazio et al. also found a generalization asymmetry in favor of negative attitudes versus positive ones. They attributed it to a negativity bias that “weights resemblance to a known negative more heavily than resemblance to a positive” (p. 300). Applied to help seeking scenarios, this would mean that when facing an unknown situation, people would tend to generalize and infer that is more likely that they get a negative rather than a positive outcome from it, so, along with what it was said before, people will be more inclined to think that they will get a “no” when requesting help. Denrell and Le Mens (2011) present a different perspective when trying to explain judgment biases in general. They deviate from the classical inappropriate information processing (depicted among other by Fiske & Taylor, 2007, and Tversky & Kahneman, 1974) and explain this in terms of ‘adaptive sampling’. Adaptive sampling is a sampling mechanism in which the selection of sample items is conditioned by the values of the variable of interest previously observed (Thompson, 2011). Sampling adaptively allows individuals to safeguard themselves from experiences they went through once and turned out to lay negative outcomes. However, it also prevents them from giving a second chance to those experiences to get an updated outcome that could maybe turn into a positive one, a more positive one, or just one that regresses to the mean, whatever direction that implies. That, as Denrell and Le Mens (2011) explained, makes sense: If you go to a restaurant, and you did not like the food, you do not choose that restaurant again. This is what we think could be happening when asking for help: When we get a “no”, we stop asking. And here, we want to provide a complementary explanation for the underestimation of the probability that others comply with our direct help requests based on adaptive sampling. First, we will develop and explain a model that represents the theory. Later on, we will test it empirically by means of experiments, and will elaborate on the analysis of its results.
Resumo:
Background: The ratio of the rates of non-synonymous and synonymous substitution (d(N)/d(S)) is commonly used to estimate selection in coding sequences. It is often suggested that, all else being equal, d(N)/d(S) should be lower in populations with large effective size (Ne) due to increased efficacy of purifying selection. As N-e is difficult to measure directly, life history traits such as body mass, which is typically negatively associated with population size, have commonly been used as proxies in empirical tests of this hypothesis. However, evidence of whether the expected positive correlation between body mass and d(N)/d(S) is consistently observed is conflicting. Results: Employing whole genome sequence data from 48 avian species, we assess the relationship between rates of molecular evolution and life history in birds. We find a negative correlation between dN/dS and body mass, contrary to nearly neutral expectation. This raises the question whether the correlation might be a method artefact. We therefore in turn consider non-stationary base composition, divergence time and saturation as possible explanations, but find no clear patterns. However, in striking contrast to d(N)/d(S), the ratio of radical to conservative amino acid substitutions (K-r/K-c) correlates positively with body mass. Conclusions: Our results in principle accord with the notion that non-synonymous substitutions causing radical amino acid changes are more efficiently removed by selection in large populations, consistent with nearly neutral theory. These findings have implications for the use of d(N)/d(S) and suggest that caution is warranted when drawing conclusions about lineage-specific modes of protein evolution using this metric.
Resumo:
State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.
Resumo:
State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.
Resumo:
State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.
Resumo:
State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.