To solve these exercises, we need to:

1. Use linear regression to find the equation of the best-fit line for each set of data points.
2. Find and interpret the correlation coefficient for each dataset.

Let me break this down for each scatter plot (19-24):

19. Scatter Plot Analysis:

• The points appear somewhat random and spread out with no clear trend.
• Correlation: Likely very low or close to 0 since the data does not show a strong linear relationship.
• Best-fit line: Hard to determine a clear line since there's little correlation.

20. Scatter Plot Analysis:

• The points show a positive linear trend.
• Correlation: Positive and high (close to 1), meaning the data points are closely following a linear upward trend.
• Best-fit line: The line will have a positive slope.

21. Scatter Plot Analysis:

• The points are a bit more spread out, but there is still a slight positive trend.
• Correlation: Moderate positive correlation, likely between 0 and 1, but less than in plot 20.
• Best-fit line: Positive slope but not as steep as in 20.

22. Scatter Plot Analysis:

• The points show a negative linear trend, moving downwards.
• Correlation: Negative and high (close to -1), indicating a strong negative relationship.
• Best-fit line: The line will have a negative slope.

23. Scatter Plot Analysis:

• The points are randomly scattered, similar to 19, with no clear trend.
• Correlation: Likely very low or close to 0 due to the lack of any linear relationship.
• Best-fit line: No significant line of best fit.

24. Scatter Plot Analysis:

• The points show a negative linear trend, although more scattered than 22.
• Correlation: Negative but moderate, likely between 0 and -1.
• Best-fit line: Negative slope but not as steep as in plot 22.

General Summary:

• Correlation coefficients will range from -1 to 1, with 1 being a perfect positive correlation, -1 being a perfect negative correlation, and 0 meaning no correlation.

Do you want to calculate the exact equations and correlation coefficients for each plot?