Advanced Placement (AP), 30.11.2020 16:10 asims44

1. Collect two separate pieces of Quantitative data from your classmates, which you will compare statistically to determine possible association. (You should
have about 15 data points total.)

2. Which variable is your explanatory variable? Which is your response? Why
did you choose them in this way?

3. List the appropriate summary statistics for each of your data sets (1-VAR-
STATS).

4. Draw a Scatterplot of your data by hand. Make sure it is neat and properly
labeled.

5. Describe the association shown in your scatterplot. Be sure to mention the 5
important aspects: Strength, Direction, Form, Outliers, and Context.

6. Use STAT -> CALC -> LINREG to determine slope, y-intercept, R-squared, and
correlation (r).

7. Write out your linear model using the proper notation. Use this model to
predict two other students that aren’t in your current data set.

8. Use the formulas on the formula sheet to find the linear model by hand. Show
all work.

9. Interpret the linear model in context. More specifically, interpret the slope
and y-intercept in context of the situation.

10. Show your LSRL on your scatterplot. Graph it neatly, and clearly label the
point (x-bar, y-bar).

11. Correctly calculate the residual for each of your data values. Explain, in
context, what it means for a residual to be positive and for a residual to be
negative.

12. Create a residual plot for your data set and correctly interpret this plot.

13. Do you think your linear model is a useful model? Explain, using statistical
evidence to support your claim.

14. Calculate and correctly interpret R-squared in the context of your situation.

15. Switch your explanatory and response variables. Create a new linear model
predicting in the other direction. Correctly interpret the slope and y-intercept of this new model.

16. Correlation does not imply causation. Explain exactly what this means, and
apply it to your situation in context.

17. Explain why, in real life, you should never extrapolate. Explain why
extrapolation might be a dangerous thing to do with your specific data.

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