Exercise

# Simplify the model with p-values

Leonardo da Vinci once said: "Simplicity is the ultimate sophistication." It also applies to data science modeling. In this exercise, you will practice using the p-values to decide the necessity of model parameters, and define a parsimonious model without insignificant parameters.

The null hypothesis is the parameter value is zero. If the p-value is larger than a given confidence level, the null hypothesis cannot be rejected, meaning the parameter is not statistically significant, hence not necessary.

A GARCH model has been defined and fitted by the Bitcoin return data. The model result is saved in `gm_result`

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Instructions 1/2

**undefined XP**

- Print the model fitting summary.
- Get the model parameters and p-values, and save them in a DataFrame
`para_summary`

. - Print and review
`para_summary`

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