bootstrap-method-for-error-estimation

Tools used:

Python 3 on Ubuntu 18.04 and Core i7-9750 (CPU @2.60GHz x 12).

Type of data

The main dataset is made up by IQ. list of 40 sample and each between 30 and 170.

Result of computing

How to use the Class

Tasks you have TO DO.

• Step [1] : DATASET GENERATION

estim = Estimator(default=40) # 40 is the default lenght of the set
counter = 0         # important for the next step
maximum = 10000     # you have to set your max number for iteration

• Step [5] : REPEAT STEP 2 though 4 10000 times (start of loop)

while counter < maximum
    # step [2,3] := RANDOM PICK UP FOR ALL SAMPLES(default=40)
    estim.pick()
    # step [4] := CAULCULATE our SAMPLE STATISTIC(mean and median)
    estim.mean_median[0].append(estim.calculate_mean(estim.pick()))
    estim.mean_median[1].append(estim.calculate_median(estim.pick()))

• Step [6] := CAULCULATE STANDARD DEVIATION of distribution of the 10,000 means and medians

estim.calculate_SD(estim.mean_median[0]) # SD of mean
estim.calculate_SD(estim.mean_median[1]) # SD of median

• Step [7] := CAULCULATE 2.5th and 97.5th centiles of the 10,000 means and medians

mean_of_mean = estim.calculate_mean(estim.mean_median[0])
mean_of_median = estim.calculate_mean(estim.mean_median[1])
sd_of_mean = estim.calculate_SD(estim.mean_median[0])
sd_of_median = estim.calculate_SD(estim.mean_median[1])

estim.small_centile([mean_of_mean, sd_of_mean])       # 2.5th centile of 10000 means
estim.big_centile([mean_of_mean, sd_of_mean])         # 97.5th centile of 10000 means
estim.small_centile([mean_of_median , sd_of_median])  # 2.5th centile of 10000 medians
estim.big_centile([mean_of_median , sd_of_median])    # 97.5th centile of 10000 medians

Credit to this.

This LINK help me to understand the fundamental notion of bootstrap method.