HELP ME PLEASE! HALF-DONE ANSWERS WILL BE DELETED! 15 POINTS!

G) Does your residual plot show that the linear model from the regression calculator is a good model? Explain your reasoning.

H) Using your equation from step 2d, estimate the GPA of a student who studies for 15 hours a week. Justify your answer.

** Residual Plot and Scatterplot below **
** Step 2d --> 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. (I didn't have a regression calculator) **

G) Yes, because the plots and the linear model both align to produce a similar calculated sum.

H) I need to see the data table again for step 2d.

Step-by-step explanation:

1.) You scatter plot should be off by 6.97, since that was the first difference in your data table set of terms.

Basically subtract all of the GPAs from the Hours in the table.

Ex). Hours - GPA = Difference

or like before,

      9.2 - 2.23 = 6.97

Do the rest of the numbers like this then plot the answers.  I'd advise you plot your second set of scatter plot points in a different color.

G: The residual plot given displays the linear model from the regression calculator as being a good model for it represets the points and how the linear model compared to the residual model calculates to a similar, if not same result.

Based on the equation we did for 2d:

H: We can interpret the average or close accurate of a student that studies for 15 hours a week to be around 3.92 based on previous findings. Along with that, the scatterplot should be different by the first set of differences that was subtracted.

When plotting the new points, be sure to make them a seperate color of shape (depending on how creative you wish to be) so you can tell the two apart form one another.

Hope this Helps!

D

Step-by-step explanation:

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