# #StackBounty: #r #regression #python #linear Which is the dependent variable?

### 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?

Get this bounty!!!

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