Real life example for linear regression: advertising spending and revenue



Businesses often use linear regression to understand the relationship between advertising spending and revenue.

For example, they might fit a simple linear regression model using advertising spending as the predictor variable and revenue as the response variable. The regression model would take the following form:

revenue = β0 + β1(ad spending)

The coefficient β0 would represent the total expected revenue when ad spending is zero.

The coefficient β1 would represent the average change in total revenue when ad spending is increased by one unit (e.g. one dollar).

If β1 is negative, it would mean that more ad spending is associated with less revenue.

If β1 is close to zero, it would mean that ad spending has little effect on revenue.

And if β1 is positive, it would mean more ad spending is associated with more revenue.

Depending on the value of β1, a company may decide to either decrease or increase their ad spending.


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