AP®︎ Precalculus: Topic 2.6 Practice Test
Prepare for your quiz, test, or the AP exam with focused practice questions on Topic 2.6 of AP Precalculus – Competing Function Model Validation.
Questions List
Topic 2.6
Question 1 Easy
The table gives actual values of and the values predicted by a model.
| 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|
| Actual | 48.2 | 52.9 | 57.4 | 68.3 | 72.9 |
| Predicted | 47.5 | 53.8 | 60.1 | 66.4 | 72.7 |
What is the residual at ?
What You’re Being Tested On:
Explore the learning objectives taken directly from the College Board’s AP® Precalculus Curriculum. Ensure you’re prepared for the exact topics covered on the AP® exam, in-class tests, and quizzes, and gain confidence in your mastery of the material.
Topic 2.6: Competing Function Model Validation
Learning Objective: 2.6.A
Construct linear, quadratic, and exponential models based on a data set.
Essential Knowledge: 2.6.A.1
Two variables in a data set that demonstrate a slightly changing rate of change can be modeled by linear, quadratic, and exponential function models.
Essential Knowledge: 2.6.A.2
Models can be compared based on contextual clues and applicability to determine which model is most appropriate.
Learning Objective: 2.6.B
Validate a model constructed from a data set.
Essential Knowledge: 2.6.B.1
For a given value of an independent variable, a residual of a regression is the actual value of the dependent variable value minus the value predicted by the function regression model. A residual plot is a graphical representation of the residuals of a regression, with the residuals on the vertical axis and the independent variable on the horizontal axis. A model is justified as appropriate for a data set if the residual plot appears without pattern.
Essential Knowledge: 2.6.B.2
For given values of an independent variable, the errors in a model can relate to the signed differences in the values of the dependent variable predicted by the function regression model and the actual values OR the unsigned absolute values of the residuals. Errors may reveal that the model underestimates or overestimates actual values on a given input-value interval. The data set and context can provide clues as to whether underestimates or overestimates are preferred on a given input-value interval.
