The data in the table below represents the square footage and rents​ (dollars per​ month) for apartments in a section of a large city. Complete parts​ (a) through​ (c) below.
Square Footage comma x
688
769
811
795
809
901
802
Rent per Month comma Upper R
1545
1605
1620
1640
1635
1730
1650
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Part 1
​(a) Using a graphing​ utility, draw a scatter diagram of the data treating square footage as the independent variable. What type of relation appears to exist between square footage and​ rent?

The scatter plot shows a positive relationship between square footage and rent. As square footage increases, the rent generally tends to increase as well. This suggests a **positive linear correlation** between these two variables.

Would you like more details on interpreting this plot or moving forward with the next parts? 

Here are 5 related questions to expand your understanding:
1. How can you determine the strength of the correlation from the scatter plot?
2. What factors might affect rent besides square footage?
3. How can we mathematically describe the relationship between square footage and rent?
4. What statistical methods can confirm the linear relationship between two variables?
5. What is the importance of outliers in a scatter plot analysis?

**Tip:** Always check if the scatter plot suggests a linear, quadratic, or another type of relationship to choose the right model for analysis.

The data in the table below represents the square footage and rents​ (dollars per​ month) for apartments in a section of a large city. Complete parts​ (a) through​ (c) below. Square Footage: 688, 769, 811, 795, 809, 901, 802; Rent per Month: 1545, 1605, 1620, 1640, 1635, 1730, 1650. Part 1: (a) Using a graphing​ utility, draw a scatter diagram of the data treating square footage as the independent variable. What type of relation appears to exist between square footage and​ rent?

Analyze the relationship between square footage and rent using scatter plots. Explore how square footage correlates with rent prices, calculate the linear relationship, and understand key statistical concepts like correlation and regression. This problem involves data analysis suitable for students in Grades 10-12.

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