user: I'm working on a project that involves predicting future trends based on past data. Could you calculate the projected y-values using a linear regression model for the following sets of points: for x-coordinates [1, 2, 3] and y-coordinates [4, 5, 6], estimate y at x=10; for x-coordinates [2, 4, 6] and y-coordinates [8, 10, 12], estimate y at x=15; for x-coordinates [3, 6, 9] and y-coordinates [12, 15, 18], estimate y at x=20; and for x-coordinates [4, 8, 12] and y-coordinates [16, 20, 24], estimate y at x=25?

Available functions:
name: linear_regression
description: Finds the linear regression of a set of points and evaluates it at a given point.
parameters: type: dict
properties: x: type: array
description: The x coordinates of the points.
items: type: integer
y: type: array
description: The y coordinates of the points.
items: type: integer
point: type: integer
description: The point to calculate the linear regression at.
required: x
y
point

Ground truth function call(s):
linear_regression(x=[1,2,3],y=[4,5,6],point=10)
linear_regression(x=[2,4,6],y=[8,10,12],point=15)
linear_regression(x=[3,6,9],y=[12,15,18],point=20)
linear_regression(x=[4,8,12],y=[16,20,24],point=25)