Let's break down the problem step by step.

Part 1: Birthrates vs. Female Life Expectancies

You have bivariate data with birthrate ($x$) and female life expectancy ($y$) for twelve countries. The regression line equation is given as:

$y = 83.19 - 0.49x$

The equation shows that there's a negative relationship between birthrate and female life expectancy, meaning as the birthrate increases, female life expectancy decreases.

Question: Birthrates greater than the mean vs. Female Life Expectancies

The question asks whether birthrates greater than the mean are paired with female life expectancies that are above or below the mean.

Since the slope of the regression line ($-0.49$) is negative, birthrates greater than the mean tend to be paired with female life expectancies that are below the mean. This is because higher birthrates correlate with lower life expectancies.

Part 2: Interpretation of the Slope

The slope of the regression line is $-0.49$. This value indicates that for every increase of 1 birth per 1000 people in the birthrate, female life expectancy decreases by 0.49 years.

So, the answer is:

• For an increase of 1 birth per 1000 people in the birthrate, female life expectancy decreases by 0.49 years.

Summary:

• Birthrates greater than the mean are paired with female life expectancies that are below the mean.
• For each 1 birth per 1000 people increase in birthrate, female life expectancy decreases by 0.49 years.

Would you like a more detailed explanation of these concepts or further exploration of the data?

5 Related Questions:

1. How do outliers in the data affect the regression line?
2. What does the y-intercept (83.19) represent in this context?
3. How might other factors (e.g., GDP per capita) influence the relationship between birthrate and life expectancy?
4. What would be the predicted female life expectancy for a country with a birthrate of 35 births per 1000 people?
5. How does the strength of the correlation affect our confidence in using this model for predictions?

Tip: Always check the scatter plot for the data to visually inspect how well the regression line fits the data points. This can give you insight into the reliability of the model.