Interpreting the intercept of a regression line
In this section, we’ll explore the importance of interpreting the intercept of a regression line, which provides crucial context for understanding the baseline level of the dependent variable when the independent variable is equal to zero. We’ll investigate various examples to demonstrate the practical relevance of interpreting the intercept in linear regression models.
In our equation for a simple linear regression line, we have the following:
y = a + bx
The intercept, a, represents the expected value of the dependent variable, y, when the independent variable, x, is equal to zero.
Let’s examine some examples to better understand the interpretation of the intercept.
Example 1: An energy provider has developed a simple linear regression model to predict monthly electricity bills based on the number of kilowatt-hours (kWh) consumed by a household. The equation of the line of best fit is as follows:
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