AP®︎ Precalculus: Topic 2.15 Practice Test

Prepare for your quiz, test, or the AP exam with focused practice questions on Topic 2.15 of AP Precalculus – Semi-log Plots.


Questions List

Topic 2.15

Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Q15

Question 1 Easy

A data point (2,1000)(2,1000) is plotted on a semi-log plot whose y-axis is logarithmically scaled with base 10 and labeled with output values. The marks labeled 1 and 10 are one vertical unit apart. How many vertical units above the mark labeled 1 is the point plotted?

A22
B33
Cln⁡1000\ln 1000
D10001000

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 2.15: Semi-log Plots

Learning Objective: 2.15.A

Determine if an exponential model is appropriate by examining a semi-log plot of a data set.

Essential Knowledge: 2.15.A.1

In a semi-log plot, one of the axes is logarithmically scaled with logarithm base nn, where n>1n > 1. When the yy-axis of a semi-log plot is logarithmically scaled, data or functions that demonstrate exponential characteristics will appear linear.

Essential Knowledge: 2.15.A.2

If for large input values of a bivariate data set the logarithms of the output values trend linear, then transformations of an exponential function can be used to model the data set. Therefore, an advantage of semi-log plots is that the dependent variable values do not need to be transformed by a constant to reveal that transformations of an exponential function would produce an appropriate model.

Learning Objective: 2.15.B

Construct the linearization of exponential data.

Essential Knowledge: 2.15.B.1

Techniques used to model linear functions can be applied to a semi-log graph.

Essential Knowledge: 2.15.B.2

For an exponential model of the form y=abxy = ab^x, the corresponding linear model for the semi-log plot is y=(log⁡nb)x+log⁡nay = (\log_n b)x + \log_n a, where n>0n > 0 and n≠1n \ne 1. Specifically, the linear rate of change is log⁡nb\log_n b, and the initial linear value is log⁡na\log_n a.