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.
Este exercício faz parte do curso
Introduction to Linear Modeling in Python
Instruções do exercício
- Inspect the predefined data arrays,
timesanddistances, and the preloaded plot. - Based on your rough inspection, estimate the
distance_traveledaway from the starting position as ofelapse_time = 2.5hours. - Assign your answer to
distance_traveled.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# 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))