Get startedGet started for free

Reasons for Modeling: Interpolation

One common use of modeling is interpolation to determine a value "inside" or "in between" the measured data points. In this exercise, you will make a prediction for the value of the dependent variable distances for a given independent variable times that falls "in between" two measurements from a road trip, where the distances are those traveled for the given elapse times.

context figure

This exercise is part of the course

Introduction to Linear Modeling in Python

View Course

Exercise instructions

  • Inspect the predefined data arrays, times and distances, and the preloaded plot.
  • Based on your rough inspection, estimate the distance_traveled away from the starting position as of elapse_time = 2.5 hours.
  • Assign your answer to distance_traveled.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute the total change in distance and change in time
total_distance = ____[-1] - ____[0]
total_time = ____[-1] - ____[0]

# Estimate the slope of the data from the ratio of the changes
average_speed = total_distance / total_time

# Predict the distance traveled for a time not measured
elapse_time = 2.5
distance_traveled = average_speed * elapse_time
print("The distance traveled is {}".format(distance_traveled))
Edit and Run Code