The dataset contains the following columns:

1. Price/Book Value Ratio: The target variable we need to predict.
2. Return on Equity: Independent variable $X_1$.
3. Growth%: Independent variable $X_2$.

I will now develop a regression model to predict the Price-to-Book Value Ratio using these two predictors. Let's calculate the coefficients and intercept of the regression equation.The regression model to predict the Price-to-Book Value Ratio ($\hat{Y_i}$) based on Return on Equity ($X_{1i}$) and Growth ($X_{2i}$) is given by the equation:

$\hat{Y_i} = 1.941 + 0.0598 \cdot X_{1i} + 0.022 \cdot X_{2i}$

This equation suggests:

• For each additional unit increase in Return on Equity, the Price-to-Book Value Ratio increases by approximately 0.0598 units, holding Growth constant.
• For each additional unit increase in Growth, the Price-to-Book Value Ratio increases by approximately 0.022 units, holding Return on Equity constant.

Would you like more details or have any questions about this model?

Here are 5 related questions to consider:

1. How accurate is this model in predicting Price-to-Book Value Ratio?
2. What is the R-squared value for this regression model?
3. Can we test the significance of each predictor variable?
4. How do we interpret the coefficients in a practical business context?
5. Are there any outliers or influential points that affect the model?

Tip: When interpreting regression coefficients, consider the scale and unit of each variable to understand their impact accurately.