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The intercept $b_0$ is calculated by substituting the mean point $(\bar{x}, \bar{y})$ and the known slope $b_1$ into the regression equation $\hat{y} = b_0 + b_1x$, and then solving algebraically for $b_0$. In the example, substituting $x=3$, $\hat{y}=4$, and $b_1=0.6$ yields $4 = b_0 + 0.6(3)$, which simplifies to $b_0 = 2.2$.

Conditions: The slope $b_1$ has already been computed.; The sample means $\bar{x}$ and $\bar{y}$ are known.; The regression line is assumed to pass through $(\bar{x}, \bar{y})$.

The intercept $b_0$ is calculated by substituting the mean point $(\bar{x}, \bar{y})$ and the known slope $b_1$ into the regression equation $\hat{y} = b_0 + b_1x$, and then solving algebraically for $b_0$. In the example, substituting $x=3$, $\hat{y}=4$, and $b_1=0.6$ yields $4 = b_0 + 0.6(3)$, which simplifies to $b_0 = 2.2$.

Conditions: The slope $b_1$ has already been computed.; The sample means $\bar{x}$ and $\bar{y}$ are known.; The regression line is assumed to pass through $(\bar{x}, \bar{y})$.

The final fitted regression equation is $\hat{y} = 2.2 + 0.6x$. This is assembled by substituting the computed intercept $b_0 = 2.2$ and the computed slope $b_1 = 0.6$ into the general linear model $\hat{y} = b_0 + b_1x$.

Conditions: The data set consists of the five specific points (1,2), (2,4), (3,5), (4,4), and (5,5).; The slope $b_1$ has been calculated as 0.6.; The intercept $b_0$ has been calculated as 2.2.

The final fitted regression equation is $\hat{y} = 2.2 + 0.6x$. This is assembled by substituting the computed intercept $b_0 = 2.2$ and the computed slope $b_1 = 0.6$ into the general linear model $\hat{y} = b_0 + b_1x$.

Conditions: The data set consists of the five specific points (1,2), (2,4), (3,5), (4,4), and (5,5).; The slope $b_1$ has been calculated as 0.6.; The intercept $b_0$ has been calculated as 2.2.