Based on the image provided, let's analyze the Least Squares Regression model output and answer the questions.

B) Write the Least Squares Regression Line:

The regression line is based on the equation:

$\hat{y} = b_0 + b_1 x$

Where:

• $\hat{y}$ is the predicted dependent variable (in this case, we are likely predicting some outcome based on knee height),
• $b_0$ is the y-intercept (constant term),
• $b_1$ is the slope (coefficient of the independent variable).

From the table:

• The constant (intercept) $b_0 = 53.157$,
• The Knee Height coefficient $b_1 = 2.225$.

So, the least squares regression line is:

$\hat{y} = 53.157 + 2.225 \times \text{Knee Height (cm)}$

C) Identify and Interpret the Slope:

The slope $b_1 = 2.225$ represents the change in the predicted $y$-value for each unit increase in the Knee Height (cm). Specifically, for every 1 cm increase in knee height, the predicted value of $y$ increases by 2.225 units.

Would you like further clarification or more detailed explanations?

Here are 5 related questions that can expand your understanding:

1. How is the least squares regression line calculated from data points?
2. What does the p-value (P) tell us about the significance of the coefficients?
3. What is the interpretation of the intercept in this context?
4. How does knee height affect the prediction if the slope was negative?
5. How does the standard error (SE Coef) impact the reliability of the regression model?

Tip: The slope tells us the strength and direction of the relationship between the independent and dependent variables. Always check its significance using the p-value.