*Bounty: 50*

*Bounty: 50*

I was looking at this Data Science question on TestDome.

The problems is stated as the following:

Implement the desired_marketing_expenditure function, which returns

the required amount of money that needs to be invested in a new

marketing campaign to sell the desired number of units.Use the data from previous marketing campaigns to evaluate how the

number of units sold grows linearly as the amount of money invested

increases.For example, for the desired number of 60,000 units sold and previous

campaign data from the table below, the function should return the

float 250,000.

Approaching this with linear regression I see this as:

`marketing_expenditure = coeff * units_sold + intercept + error`

because what I’m trying to find is the `marketing expenditure`

given a number of `units sold`

.

However the author of this test seems it has seen the `marketing expenditure`

as the independent variable, in other words:

`units_sold = coeff * marketing_expenditure + intercept + error`

from which then it calculates the `marketing_expenditure`

by rearranging the equation.

The two approaches are not equivalent and give different results as depending on what is the dependent / independent variable the linear regression algorithm tries to minimise different square distances to different regression lines.

Which approach is correct and why?