Question

The Paralyzed Veterans of America receives donations every year. They want to understand which of its donors are more likely to donate again, so they take a random sample of 1400 donors and compile them into the PVA

_
small.xlsx data set.
They use the response variable as Time.Between.Gifts.
The factors of interest are: Own.Home., Num.Children, Sex, and Total.Wealth.
What is the first split?

(
Note: You don't get the split probability when the variable is continuous. You can continue with the analysis but skip the step to select "Show the split probabilities.
)

Group of answer choices
Num.Children
>
=
4
,
Num.Children
<
4
Own.Home.
(
H
)
,
Own.Home.
(
U
)
Total.Wealth
>
=
2
,
Total.Wealth
<
2
Num.Children
<
2
,
Num.Children
>
=
2

Answer 1

Without the actual data set or analysis output, it's not possible to provide the exact first split in analyzing the donor data of the Paralyzed Veterans of America. The first split will be a variable that effectively segments the donors based on the time between their donations. The particular split is usually determined using statistical software.

Step-by-step explanation:

The first split in the analysis of the Paralyzed Veterans of America's donor data would be based on the variable that best separates donors into groups according to the time between their donations. Without access to PVA_small.xlsx or the results of the analysis, such as a decision tree output, providing an exact first split is not possible. The variables mentioned, Own.Home., Num.Children, Sex, and Total.Wealth are all potential candidates for a first split in a decision tree analysis.

The specific split would depend on which variable effectively reduces the heterogeneity of the response variable, Time.Between.Gifts. In practice, statistical software would be used to determine the optimal first split by calculating reduction in variance or another appropriate measure for deciding how to segment the data. The factors of interest - 'Own.Home.', 'Num.Children', 'Sex', and 'Total.Wealth' - would each be evaluated for their influence on the response variable.