请问这道题怎么理解

问题如下：

Your second-round interview for the Junior Quantitative Analyst position went well, and the next day, you receive an email from the investment firm congratulating you for making it this far. You are one of four remaining candidates from more than 100 who applied for the position. Because the position involves quantitative analysis, you are given an assignment to complete within 72 hours. You are provided a dataset and tasked with creating two logistic regression models to predict whether an ETF will be a “winning” fund, that is, whether the ETF’s monthly return will be one standard deviation or more above the mean monthly return across all ETFs in the dataset, or whether the ETF will be an “average” fund.

The variables in the dataset are as follows:

For the first logistic regression, you are asked to use all the independent variables, except for the fund size dummy variables (small_fund and medium_fund). For the second logistic regression, you are asked to use all the independent variables except the fund size continuous variable (net assets).

You use a standard software package (in Python or R) to develop the logistic regression models. Your results are as follows:

Based on the output from with Logistic Regression 1, how will the change in the probability that an ETF will be a winning fund increase if one of the other independent variable values, except for net_assets, is decreased by one unit, holding all else constant?

选项：

TTe probability will increase, but not as much as with the price-to-earnings increasing by one unit.

TTe probability will increase more than the price-to-earnings increasing by one unit.

TTe probability will not increase.

解释：

B is correct. In the previous question, the price-to-earnings variable value and the coefffcient are both positive. By increasing the variable value incrementally by one, we are increasing the overall positive value of the series of items in the exp function. TTerefore, if we are reducing the product of a coefffcient value pair that is negative, we are increasing the overall value of the series of items in the exp function. TTe next step is to look to see how many negative coefffcient and value products are in the series of items in the exp function, then calculate the coefffcient value product, and compare them to the coefffcient value product for the price-to-earnings variable.

Therefore, as the portfolio_bonds variable increases by one unit, it results in a larger increase in profft than the price-to-earnings variable (0.1113 versus 0.0292), since its product is larger than the price-to-earnings product increase by one unit.