`b_(yx)` is the ….. of regression line of y on x

Answer

To solve the question, we need to understand the concept of regression lines and specifically what \( b_{yx} \) represents in the context of the regression line of \( y \) on \( x \).

### Step-by-Step Solution:

1. **Understanding Regression Lines**:
- A regression line is a statistical tool used to predict the value of one variable based on the value of another variable. In this case, we are looking at the regression line of \( y \) on \( x \).

2. **General Formula for Regression Line**:
- The regression line of \( y \) on \( x \) can be expressed using the formula:
\[ y - \bar{y} = b_{yx}(x - \bar{x}) \]
- Here, \( \bar{y} \) is the mean of \( y \), \( \bar{x} \) is the mean of \( x \), and \( b_{yx} \) is the coefficient that represents the relationship between \( x \) and \( y \).

3. **Identifying \( b_{yx} \)**:
- In the equation, \( b_{yx} \) is the coefficient that indicates how much \( y \) changes for a unit change in \( x \). This coefficient is known as the **slope** of the regression line.

4. **Conclusion**:
- Therefore, we can conclude that \( b_{yx} \) is the **slope** of the regression line of \( y \) on \( x \).

### Final Answer:
\( b_{yx} \) is the **slope** of the regression line of \( y \) on \( x \).