Unit 3 Project by Elisa Glenn

Question(s)

elisaglenn9 — 2024-09-24 20:00:28 UTC

Is there a relationship between household size and household water usage? Are there an mediating or confounding variables that help explain the story? What does the r value tell us in our least squares regression line?

Introduction:

elisaglenn9 — 2024-09-24 20:07:41 UTC

The purpose of this project is to determine the correlation between household size and household water usage. The data used came from a class study.

Section B

elisaglenn9 — 2024-09-24 20:10:56 UTC

Line of Regression

elisaglenn9 — 2024-09-26 19:43:10 UTC

The line of regression represents how correlated the 2 variables are. This means that the higher the r^2 squared value is the more similar the variables are

elisaglenn9 — 2024-09-26 19:46:31 UTC

On this graph, you can see that the data point representing my household is beneath the line of best fit, which means that my household uses less than the average amount of water per day.

elisaglenn9 — 2024-09-26 19:49:52 UTC

Line of Regression Equation (And What it Means)

elisaglenn9 — 2024-09-27 19:26:39 UTC

The equation for the line of regression is y=44.6x+247. The slope of the equation (44.6), means that for every person in the house the water usage goes up by 44.6 gallons a day. This contrasts to data published by the United States Environmental Protection Agency, which say that each person uses around 82 Gallo's of water per day. The y intercept means that the data starts at 247 gallons, essentially that if 0 people lived in a house it would use 247 gallons per day

Section C

elisaglenn9 — 2024-09-27 19:29:35 UTC

Section A

elisaglenn9 — 2024-09-27 19:30:10 UTC

Water Usage and # of People

elisaglenn9 — 2024-10-04 16:07:34 UTC

According to class data, the relationship between water usage and number of people has a weak correlation, with an r^2 value of only 0.21. In order for it to be considered a strong correlation, the value needs to be above 0.35

Lateral Reading

elisaglenn9 — 2024-10-04 16:41:43 UTC

Using lateral reading, a method to ensure the data and information provided on websites is accurate, I can gather that the number of people living in a home and the gallons of water used per day has a stronger correlation than the one shown by our class data.

Relationships

elisaglenn9 — 2024-10-04 16:49:27 UTC

The relationship between these two variables is causal, meaning that one variable influences or causes change in another variable. In this case, the number of people in a home causes a change in the amount of water used daily.

Section D

elisaglenn9 — 2024-10-04 16:49:42 UTC

elisaglenn9 — 2024-10-04 19:20:02 UTC

The two variables being compared in this graph is the number of showers taken per day, and the amount of water (in gallons) used per day in one household. As you can see, the r^2 value in .45, which can now be considered a strong correlation. However, this relationship might now be causal, but however spurious. The real correlation might be between the number of people in the home, which will increase or decrease the amount of showers

elisaglenn9 — 2024-10-04 19:44:02 UTC

The graph shows the correlation between the length of showers (total minutes the showers runs) per day, and total water used per day. Despite the strong correlation between number of showers per day, the the r^2 value when comparing length of showers and daily water usage is .002, which is virtually no correlation. This would mean that the number of showers per day doesn't necessarily have as strong of a correlation as the class data suggests

City Data

elisaglenn9 — 2024-10-08 19:12:06 UTC

We can use the City Data, spreadsheet with water usage data from many cities to predict water usage from a specific household, city and even state. The average person uses 82 gallons of water per day, and taking that into account, the city of Roseville should use about 13,339,268 gallons of water per day

Our Data

elisaglenn9 — 2024-10-08 19:17:06 UTC

The data we gathered in class, and the model equation we found should not be used to predict the water usage of a city or a state. Because it was made by students, its not entirely accurate and could mean that the data and trends are incorrect, which we don't want.