Exercise

# What does the time index tell us?

Some data are naturally evenly spaced by time. The time series `discrete_data`

shown in the top figure has 20 observations, with one observation appearing at each of the discrete time indices 1 through 20. Discrete time indexing is appropriate for `discrete_data`

.

The time series `continuous_series`

shown in the bottom figure also has 20 observations, it is following the same periodic pattern as `discrete_data`

, but its observations are not evenly spaced. Its first, second, and last observations were observed at times 1.210322, 1.746137, and 20.180524, respectively. Continuous time indexing is natural for `continuous_series`

, however, the observations are approximately evenly spaced, with about 1 observation observed per time unit. Let's investigate using a discrete time indexing for `continuous_series`

.

Instructions

**100 XP**

- Use
`plot(___, ___, type = "b")`

to display`continuous_series`

versus`continuous_time_index`

, its continuous time index - Create a vector 1:20 to be used as a discrete time index.
- Now use
`plot(___, ___, type = "b")`

to display`continuous_series`

versus`discrete_time_index`

- Note the various differences between the resulting figures, but the approximation appears reasonable because the overall trend remained preserved