Exercise

# Interpolation: Inbetween Times

In this exercise, you will build a linear model by fitting monthly time-series data for the Dow Jones Industrial Average (DJIA) and then use that model to make predictions for daily data (in effect, an interpolation). Then you will compare that daily prediction to the real daily DJIA data.

A few notes on the data. "OHLC" stands for "Open-High-Low-Close", which is usually daily data, for example the opening and closing prices, and the highest and lowest prices, for a stock in a given day. "DayCount" is an integer number of days from start of the data collection.

Instructions

**100 XP**

- Use
`ols()`

to`.fit()`

a model to the`data=df_monthly`

with`formula="Close ~ DayCount"`

. - Use
`model_fit.predict()`

on both`df_monthly.DayCount`

and`df_daily.DayCount`

to predict values for the monthly and daily`Close`

prices, stored as a new column`Model`

in each DataFrame. - Use the predefined
`plot_model_with_data`

twice, on each`df_monthly`

and`df_daily`

and compare the RSS values shown.