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The video introduces R-squared ($R^2$) as the next topic. It previews the concept using a scatter plot that labels distances involving the regression line, the mean line, and actual values, though no full formula or derivation for $R^2$ is provided in this segment.

Conditions: The least-squares regression line has just been computed.; The video is transitioning to a new statistical concept.

The video introduces R-squared ($R^2$) as the next topic. It previews the concept using a scatter plot that labels distances involving the regression line, the mean line, and actual values, though no full formula or derivation for $R^2$ is provided in this segment.

Conditions: The least-squares regression line has just been computed.; The video is transitioning to a new statistical concept.

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.