Exercise

# 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.

Instructions

**100 XP**

- 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.