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The regression output shows a least-squares regression equation that models the relationship between calories and protein content in dried beans. Here's a breakdown of the key elements:

- **Constant (Intercept)**: 2.08
- **Calories (Slope)**: 0.06297
- **Standard Error of Calories**: 0.02409
- **t-value for Calories**: 2.61
- **p-value for Calories**: 0.028
- **R-squared (R²)**: 43.2%

### Slope Interpretation:
The slope of the regression line represents the predicted change in the response variable (protein content) for each 1-unit increase in the predictor variable (calorie content). The slope given is **0.06297** for calories. This means that for every additional unit of calorie content, the predicted protein content increases by **0.06297 grams**.

### Answer to the Question:
The correct interpretation of the slope is:
- **For each 1-unit increase in the calorie content, the predicted protein content increases by 0.063 grams.**

This matches the second option:
- "For each 1-unit increase in the calorie content, the predicted protein content increases by 0.063 grams."

### Do you need any further explanation or details?

Here are some related questions:
1. What does the intercept in a regression line represent?
2. How do we interpret the R-squared value in this context?
3. What is the significance of the p-value in the regression output?
4. How does the standard error affect the reliability of the slope estimate?
5. What are potential assumptions behind a least-squares regression model?

**Tip:** Always check the p-value to ensure the predictor variable is statistically significant in the model.

Which of the following statements is a correct interpretation of the slope of the regression line?

Learn how to interpret the slope of a regression line from a statistical analysis of dried beans data. This problem involves understanding the relationship between calorie and protein content, suitable for high school students studying regression analysis.

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