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The least squares regression line of mass \( y \) (in kg) on height \( x \) (in cm) is given by the equation:

\[
y = 0.682x - 50.750
\]

This equation describes how mass relates to height, with a slope of approximately \( 0.682 \) kg per cm, and an intercept of approximately \( -50.750 \) kg.

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Here are some related questions:
1. How can we interpret the slope of the regression line?
2. How is the y-intercept meaningful in this context?
3. How would we calculate predicted mass for a height of 160 cm using this regression line?
4. What is the significance of residuals in regression analysis?
5. How do we assess the accuracy of this regression model?

**Tip**: The slope tells you how much the mass is expected to increase per unit increase in height.

Find the least squares regression line of mass on height.

Learn how to find the least squares regression line for a dataset of 7 randomly selected male students' heights (in cm) and masses (in kg). This problem involves core concepts in statistics such as linear regression and the least squares method. Suitable for Grade 10-12 students.

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