Data set hours studying gpa 0.7 1.11 21.9 3.69 18 4 16.7 3.21 18 3.7 13.8 1.42 18 3.8 18 4 15.5 3.92 16.7 3.21 step 1: collect and organize your data. ⦁ using the super survey simulator, survey 10 students of your choice and gather data. create an organized representation of your data below. ⦁ what do you think the purpose of this survey is? explain. step 2: graph your data. after organizing your data, you will now create a graphical representation of your data. ⦁ why is a scatterplot an appropriate display for this data set? ⦁ what features would need to be included on a scatterplot so that the data can be easily analyzed? make a list of needed features and explain why each is important to the graph. ⦁ next, you will make a scatterplot. name a point that will be on your scatterplot and describe what it represents. ⦁ 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. ⦁ 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. ⦁ describe the type of correlation between the two variables on your graph. how do you know? ⦁ does the correlation between the variables imply causation? explain. ⦁ how do you calculate the residuals for a scatterplot? ⦁ calculate the residuals for your scatterplot in step 2d. ⦁ create a residual plot for your data. ⦁ 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. ⦁ using your equation from step 2d, estimate the gpa of a student who studies for 15 hours a week. justify your answer.