Question

To assess the accuracy of a statistical estimate, we need to perform bootstrapping (resampling). Write a function named simulate_resample. This function should generate a resample from the observed serial numbers located in the column serial number of the dataframe obs and return that resample. Ensure that the resample is structured as a dataframe like obs. The function should take no arguments.

Note: Inside the function, once you resample the serial numbers, it will be useful to first create a dictionary named resample_dict that consists of the resampled serial numbers in a key named serial number. Use that dictionary to create a dataframe named resample_df consisting of the resampled serial numbers in a column named serial number.

A. def simulate_resample(obs):

B. def simulate_resample():

C. def simulate_resample(obs, resample_size):

D. def simulate_resample(resample_size):

Answer 1

The correct function definition for the bootstrapping statistical estimation is 'def simulate_resample()' without any arguments. It would involve creating a dictionary from the resampled serial numbers and then converting this into a structured DataFrame.

Step-by-step explanation:

To assess the accuracy of a statistical estimate via bootstrapping, which involves resampling with replacement, the correct function to write is option B: def simulate_resample(). This function does not take any arguments, in line with the specifics of the question. The process includes creating a dictionary with the resampled data and then forming a new DataFrame out of this dictionary, ensuring that the structure remains consistent with the 'obs' DataFrame. It is important to simulate a large number of resamples to create reliable saturation or rarefaction curves, which show when additional sampling ceases to provide additional information.