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The slope $b_1$ is computed by dividing the sum of the cross-products of deviations by the sum of the squared x-deviations. In the worked example, the sum of the cross-products is 6 and the sum of the squared x-deviations is 10, yielding $b_1 = 6/10 = 0.6$.

Conditions: The data set consists of paired observations.; The sample means $\bar{x}$ and $\bar{y}$ have been computed.; The denominator $\sum (x_i-\bar{x})^2$ is nonzero.

The slope $b_1$ is computed by dividing the sum of the cross-products of deviations by the sum of the squared x-deviations. In the worked example, the sum of the cross-products is 6 and the sum of the squared x-deviations is 10, yielding $b_1 = 6/10 = 0.6$.

Conditions: The data set consists of paired observations.; The sample means $\bar{x}$ and $\bar{y}$ have been computed.; The denominator $\sum (x_i-\bar{x})^2$ is nonzero.

The regression line must pass through the point (3,4) because this point represents the intersection of the sample means of the independent and dependent variables, $(\bar{x}, \bar{y})$. The video states as a general property that all regression lines have to go through the point where the mean of the independent variable and the mean of the dependent variable cross.

Conditions: The data set has a computed mean for x, $\bar{x}=3$.; The data set has a computed mean for y, $\bar{y}=4$.; The context is simple linear regression.

The regression line must pass through the point (3,4) because this point represents the intersection of the sample means of the independent and dependent variables, $(\bar{x}, \bar{y})$. The video states as a general property that all regression lines have to go through the point where the mean of the independent variable and the mean of the dependent variable cross.

Conditions: The data set has a computed mean for x, $\bar{x}=3$.; The data set has a computed mean for y, $\bar{y}=4$.; The context is simple linear regression.