# data[].attributes.body
1. You can see from the scatterplot that the data does not correlate, the dots are barely near the LSRL line
2. The LSRL line purpose is to let us know if the data correlates and if the data is increasing or decreasing
3. Slope- For every increase of the number of people in a household we would predict an increase of 21 gallons
Y- intercept- If a household has a number of zero people we would predict a usage of 426 gallons
4. My dot tells me that multiple people have a family of 6 and use around 600 gallons, since my dot is close to the LSRL line along with other dots around it
5. The r&r^2 tells us the relationship is weak because r^2 is lower than 0.175 and r is lower than 0.3

# data[].attributes.body
1. You can see from the scatterplot that the data doesn't correlate since not many of the dots are on the line
2. The purpose of the LSRL line is to tell us if the data correlates and if the data is increasing or decreasing
3. Slope- For every increase of people in a household we would predict an increase of 54 gallons
Y-intercept- If a household has zero people we would predict a usage of 339 gallons
4. My dot tells me that multiple people have the same usage of water and number of family members as me since my dot is on the LSRL line
5. The r^2&r tells us the relationship is that the relationship is weak if the r^2 is lower than .175 and the r is lower than .3

# data[].attributes.body
1. You can see from the scatterplot that the data slightly correlates
2. The purpose of the LSRL line is to tell us if the data correlates and if the data is increasing or decreasing
3. Slope- For every increase of people in a household we would predict an increase of 54 gallons
Y-intercept- If a household has a number of zero members we would predict a usage of 225 gallons
4. My dot tells me multiple families have the same water usage habits as mine, my dot is surrounded by others in the same area
5. The r^2&r tells us the r^2 relationship is weak because the r^2 is lower than 0.175 however the r is moderate since it is higher than zero

# data[].attributes.body
1. You can see from scatterplot that the data slightly correlates, dots are on the line and the rest are closely near it
2. The purpose of the LSRL line is to tell us if the data correlates and if the data is increasing or decreasing
3. Slope- For every increase of population we would predict an increase of 133 water usage
Y-intercept- If a city population is zero we would predict a usage of 76279729 total water usage
4. The dots tell us people have very similar water usage habits in their home
5. The r^2&r tells us the relationship is strong because both numbers are higher than 0.35 and 0.6