Section 13.1 Summary

1. This section examines inferential methods for regression analysis. The regression model, or the (population) regression equation, is $y = {\beta }_{1}x + {\beta }_{0 } + \epsilon$, where ${\beta }_{0 }$ is the $y$ intercept of the population regression line, ${\beta }_1$ is the slope of the population regression line, and $\epsilon$ is the error term.
2. A hypothesis test may be performed to determine whether a linear relationship exists between $x$ and $y$ .
3. We can construct confidence intervals for the true value of the population regression slope ${\beta }_1$ because it is unknown.