The phrase "a linearized regression" is correct and usable in written English.
It can be used in the context of statistical analysis or modeling, particularly when discussing a regression model that has been simplified or transformed into a linear form.
Example: "In our study, we applied a linearized regression to better understand the relationship between the variables."
Alternatives: "a linear regression model" or "a simplified regression analysis".
Additional file 1: Table S3 shows that, for all specifications of interest, the probit model provides similar results to those coming from the linearized regression models.
Since the 1990 sample used for the voting selection correction did not suffer from the same sort of sample size issues as the 1989 supplements, the linearized regression was not employed for the voting regressions.
It is also true that using the linearized regression shows stronger impacts of the inequality measures in several cases, indicating that there may indeed have been some noise arising from using the smaller 1989 CPS supplement.
Linearized regressions (results not shown) were also run using a structure similar to Table 4 (by race) and Table 5 (own-race) with a similar pattern of effects to those in Additional file 1: Table S3.
Overall, the linearized regressions provide further support in keeping with the negative relationship between the fraction foreign born in the city and social cohesion.
Namely, while results in the linearized regressions generally mirrored those in the probit regressions, the "difference" regression had the weakest effects—although, with the exception of white own-race effects, they were still significant at the 10% level if they had been significant at the 5% or the 1% in the probit structure.
Secondly, the indentation data were plotted according to the linearized form of transformed Eq. 1, ensuring that the linear regression of each portion was above 99.5 %.
Figures 6 and 7 show the plots of linearized forms; the best-fitted parameters deduced from Eqs. (4) and (5) by the linear regression analysis are listed in Table 2, along with the regression coefficients (R 2 ), which provide a measurement of the model fitness.
