Estimation of Population Parameters

Imagine a constellation ("population") of satellites orbiting for a full year, and the distance traveled in each hour is measured in kilometers. There is variation in the distances measured from hour-to-hour, due to unknown complications of orbital dynamics. Assume we cannot measure all the data for the year, but we wish to build a population model for the variations in orbital distance per hour (speed) based on a sample of measurements.

In this exercise, you will assume that the population of hourly distances are best modeled by a gaussian, and further assume that the parameters of that population model can be estimated from the sample statistics. Start with the preloaded sample_distances that was taken from a population of cars.

Compute the mean and standard deviation of the sample_distances.
Use the sample statistics, mean and stdev, good estimates for parameters mu and sigma of a population model.
Pass those values, and sample_distances, into the predefined gaussian_model() to build the population model.
Use the predefined plot_model_and_data() to plot the sample data and the population model together.

Exercise

Estimation of Population Parameters

Instructions