Hướng dẫn hypothesis testing in python datacamp github - kiểm tra giả thuyết trong python datacamp github

#----------------------------------------------------------------------------------# #Chapter 3 - Introduction to hypothesis testing #----------------------------------------------------------------------------------# #Generating a permutation sample def permutation_sample[data1, data2]: """Generate a permutation sample from two data sets.""" # Concatenate the data sets: data data = np.concatenate[[data1, data2]] # Permute the concatenated array: permuted_data permuted_data = np.random.permutation[data] # Split the permuted array into two: perm_sample_1, perm_sample_2 perm_sample_1 = permuted_data[:len[data1]] perm_sample_2 = permuted_data[len[data1]:] return perm_sample_1, perm_sample_2 #----------------------------------------------------------------------------------# #Visualizing permutation sampling for _ in range[50]: # Generate permutation samples perm_sample_1, perm_sample_2 = permutation_sample[ rain_july, rain_november] # Compute ECDFs x_1, y_1 = ecdf[perm_sample_1] x_2, y_2 = ecdf[perm_sample_2] # Plot ECDFs of permutation sample _ = plt.plot[x_1, y_1, marker='.', linestyle='none', color='red', alpha=0.02] _ = plt.plot[x_2, y_2, marker='.', linestyle='none', color='blue', alpha=0.02] # Create and plot ECDFs from original data x_1, y_1 = ecdf[rain_july] x_2, y_2 = ecdf[rain_november] _ = plt.plot[x_1, y_1, marker='.', linestyle='none', color='red'] _ = plt.plot[x_2, y_2, marker='.', linestyle='none', color='blue'] # Label axes, set margin, and show plot plt.margins[0.02] _ = plt.xlabel['monthly rainfall [mm]'] _ = plt.ylabel['ECDF'] plt.show[] #----------------------------------------------------------------------------------# #Generating permutation replicates def draw_perm_reps[data_1, data_2, func, size=1]: """Generate multiple permutation replicates.""" # Initialize array of replicates: perm_replicates perm_replicates = np.empty[size] for i in range[size]: # Generate permutation sample perm_sample_1, perm_sample_2 = permutation_sample[data_1, data_2] # Compute the test statistic perm_replicates[i] = func[perm_sample_1, perm_sample_2] return perm_replicates #----------------------------------------------------------------------------------# #Look before you leap: EDA before hypothesis testing # Make bee swarm plot _ = sns.swarmplot[x='ID', y='impact_force', data=df] # Label axes _ = plt.xlabel['frog'] _ = plt.ylabel['impact force [N]'] # Show the plot plt.show[] #----------------------------------------------------------------------------------# #Permutation test on frog data def diff_of_means[data_1, data_2]: """Difference in means of two arrays.""" # The difference of means of data_1, data_2: diff diff = np.mean[data_1] - np.mean[data_2] return diff # Compute difference of mean impact force from experiment: empirical_diff_means empirical_diff_means = diff_of_means[force_a, force_b] # Draw 10,000 permutation replicates: perm_replicates perm_replicates = draw_perm_reps[force_a, force_b, diff_of_means, size=10000] # Compute p-value: p p = np.sum[perm_replicates >= empirical_diff_means] / len[perm_replicates] # Print the result print['p-value =', p] #----------------------------------------------------------------------------------# #A one-sample bootstrap hypothesis test # Make an array of translated impact forces: translated_force_b translated_force_b = force_b - np.mean[force_b] + 0.55 # Take bootstrap replicates of Frog B's translated impact forces: bs_replicates bs_replicates = draw_bs_reps[translated_force_b, np.mean, 10000] # Compute fraction of replicates that are less than the observed Frog B force: p p = np.sum[bs_replicates = empirical_diff_means] / len[bs_replicates] print['p-value =', p] #----------------------------------------------------------------------------------# #A two-sample bootstrap hypothesis test for difference of means. # Compute mean of all forces: mean_force mean_force = np.mean[forces_concat] # Generate shifted arrays force_a_shifted = force_a - np.mean[force_a] + mean_force force_b_shifted = force_b - np.mean[force_b] + mean_force # Compute 10,000 bootstrap replicates from shifted arrays bs_replicates_a = draw_bs_reps[force_a_shifted, np.mean, size=10000] bs_replicates_b = draw_bs_reps[force_b_shifted, np.mean, size=10000] # Get replicates of difference of means: bs_replicates bs_replicates = bs_replicates_a - bs_replicates_b # Compute and print p-value: p p = np.sum[bs_replicates >= empirical_diff_means] / len[bs_replicates] print['p-value =', p] #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------# #----------------------------------------------------------------------------------#