# data[].attributes.body
The more amount of people there are in a household, the more water is being used. If fewer people are in a home, the less water is being used. The more people in a home, the more water is being used. Confounding variables in the data would be certain reasons people use water such as running water, showers, sinks, dishwashers, lawns, pools, etc. All of these usages of water have a direct impact on how much water is being used per household. I think the story being told is the amount of people using water regardless of the reason has a direct impact on how much water is used. The data has a causal relationship, the water being used has a a direct correlation with the people in the house.

# data[].attributes.body
The scatterplot shows the usage of water gallons per household and the data is very diverse. We can see that the more people there are in a household the greater the amount of water is being used. The average is anywhere from 200-450 gallons. The purpose is to show the best fit for the line and it correlates by showing how the data points mostly lie around this line. The slope 53.5741x is the average rate of change for the line. The y-int is where the line started from, so the average point where the data lies. My data was 3 people in a house with an average water usage of around 600. It is very close to the line so it has a high correlation with the least square regression line. The r-squared value (0.045) means the data has very little to no correlation being less than 0.1. The r-value (0.212) has a higher correlation but is still very low. R-squared provides the relative measure of the percentage of the dependent variable variance that the model explains.

# data[].attributes.body
I believe we won't be able to use this information to find school data because the data wouldn't have a good correlation due to everyone living in different areas/cities. We could potentially find data for our city because everyone is in the same area and the data wouldn't be as scattered. (The r-value would be higher). For the state, I think the data would be similar to RHS data because it is very diverse and spread apart so the r-value would be lower. Predictions using our district model would most likely be similar if we used this information to find the district data. Our city as an estimate uses 97,389,300.4417 gallons a day. Our state as an estimate uses 38 billion gallons of water a day. New York as comparison which has a high population uses 1 billion gallons a day. California has an extremely high water usage per day with a difference of 29 billion. I believe the city and state predictions are reasonable because the population of Roseville and the population of California are both high.

# data[].attributes.body
How does the amount of people in a house correlate to the amount of water being used? The data collected for the personal household data, class data, rjuhsd data, and city data was collected through various tactics such as google forms, google searches(city data), etc. Our class found our household water usage, which was all compiled using a spreadsheet. Further, the data was organized, and modeled on codap and stapplet.