Linear probability models are also widely used to estimate the effect of an explanatory variable on the probability of the dependent variable (e.g., [40, 41]).5 It is easier to interpret the marginal effect from linear probability models.
Since the coefficients of probit models do not necessarily represent marginal effects, we interpret our results using the marginal effect (see model 7).
However, it can be interpreted into marginal increase in terms of real truck number (1 2 vehicles) based on the parameter of annual average daily truck traffic (AADTT) for the study site.
We can approximate M′ as M′ = log (1 + M′) for M′ being small; hence the coefficients estimates in Eq. (3) may be loosely interpreted as marginal effect on the total profit impact arising from big data.
This work explores the tradeoff between signal acquisition and incoherent sampling on image reconstruction quality given prior knowledge of the image geometry for weighted random sampling schemes, finding that optimal distribution is not robustly determined by maximizing the acquired signal but from interpreting its marginal change with respect to the sub-sampling rate.
The estimates in the table correspond to a linear probability model and the coefficients can be directly interpreted as marginal effects.19 The first column shows the raw level of discrimination, where the dependent variable in the previous equation is regressed only on the immigrant indicator.
Those domains rated less often as being important should not be interpreted as marginal.
Also the ratio of coefficients can be interpreted as marginal rate of substitution (MRS) between any two attributes.
The coefficients shown in Table 7 are those for the difference-in-difference interaction between the variables PRIV and POST and can be interpreted as marginal effects.
The results portrayed are effect estimates from the binary probit model, interpreted as marginal probability effects that arise from the fully specified/fully adjusted empirical model (see Table 2).
