The regression line of X on Y is the line which is drawn such that the aggregate of the squares of the vertical deviations of the observations from the corresponding point on the line is minimum.

Answer

To determine whether the statement about the regression line of X on Y is true or false, we need to analyze the concept of regression lines and the method of least squares.

### Step-by-Step Solution:

1. **Understanding Regression Lines**:
- The regression line of X on Y is used to predict the value of X based on the value of Y. It is represented mathematically as:
\[ X = b_{xy}(Y - \bar{Y}) + \bar{X} \]
- Here, \( b_{xy} \) is the slope of the regression line, \( \bar{X} \) and \( \bar{Y} \) are the means (averages) of X and Y respectively.

2. **Slope Calculation**:
- The slope \( b_{xy} \) can be calculated using the formula:
\[ b_{xy} = r \cdot \frac{\sigma_x}{\sigma_y} \]
- Where \( r \) is the correlation coefficient between X and Y, and \( \sigma_x \) and \( \sigma_y \) are the standard deviations of X and Y respectively.

3. **Vertical Deviations**:
- The statement claims that the regression line is drawn such that the aggregate of the squares of the vertical deviations of the observations from the corresponding point on the line is minimized.
- This is actually true for the regression line of Y on X, not X on Y.

4. **Conclusion**:
- Since the statement is about the regression line of X on Y and it describes a property that applies to the regression line of Y on X, we conclude that the statement is **false**.

### Final Answer:
The statement is **false**.

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