Step 1: Collect and organize your data. a) Using the Super Survey Simulator found in the link below, survey 10 students of your choice and gather data. Create an organized representation of your data below.

Name. Hours G. P.A
Jack. 13.8. 1.42
Daniel. 20. 4
Olivia. 16.7. 3.5
Vikram. 0.7. 1.11
Hannah. 15.5. 3.92
Charlotte 19.7. 3.5
Lucy. 15.5 3.92
Ella. 9.2. 2.23
Jessica. 13.8. 1.42
Suraj. 18. 4

b) What do you think the purpose of this survey is? Explain.

To understand how the time we study equals our G. P.A. The results indicate that everyone learns at their own speed so that time does not equal grades.

Step 2: Graph your data.

I did graph

After organizing your data, you will now create a graphical representation of your data.
a) Why is a scatter plot an appropriate display for this data set?
You use a scatter plot because if you look at the data each person learns at a different speed. Everyone has their own learning abilities so it does not end up in a straight line but ends up as a scatter plot.
b) What features would need to be included on a scatter plot so that the data can be easily analyzed? Make a list of needed features and explain why each is important to the graph.
X-axis and Y-axis must be labeled, We must have a table for the data. The Y-axis will be the G. P.A. The X-axis will be how many hours that person worked. you must have at least three plot points.
c) Next, you will make a scatterplot. Name a point that will be on your scatter plot and describe what it represents.
The point I chose is (13.8,1.42) Jack. This point represents that Jack worked 13.8 hours and has a G. P.A of 1.42 which is a D.
d) Using the regression calculator in your tool bar, create a scatterplot using your data set from step 1. Insert a screenshot of your scatterplot, or recreate it below.

Step 3: Analyze your data.
Now that you have represented your data graphically, it can be more easily analyzed.
a) Describe how the line of best fit and the correlation coefficient can be used to determine the correlation between the two variables on your graph.
b) Describe the type of correlation between the two variables on your graph. How do you know? c) Does the correlation between the variables imply causation? Explain.
d) How do you calculate the residuals for a scatterplot?
e) Calculate the residuals for your scatterplot in step 2d.

f) Create a residual plot for your data.
g) Does your residual plot show that the linear model from the regression calculator is a good model? Explain your reasoning.
Step 4: Make a prediction with your data.
a) Using your equation from step 2d, estimate the GPA of a student who studies for 15 hours a week. Justify your answer.