# Example data: proportions of people liking a product in different regions proportions = np.array([0.2, 0.3, 0.1]) sample_sizes = np.array([100, 200, 50])
import numpy as np from scipy import stats
# You can now manipulate these distributions or fit more complex models The guide provided outlines a general approach to modeling like proportions. For a specific dataset or scenario (like what seems to be indicated by "-SSIS-343-Model Like Proportions-Marin Hinata.H..."), you would need to adapt these steps with more detailed information about your data and objectives.
# Create a binomial distribution for each distributions = [stats.binom(sample_sizes[i], proportions[i]) for i in range(len(proportions))]
# Example data: proportions of people liking a product in different regions proportions = np.array([0.2, 0.3, 0.1]) sample_sizes = np.array([100, 200, 50])
import numpy as np from scipy import stats -SSIS-343-Model Like Proportions-Marin Hinata.H...
# You can now manipulate these distributions or fit more complex models The guide provided outlines a general approach to modeling like proportions. For a specific dataset or scenario (like what seems to be indicated by "-SSIS-343-Model Like Proportions-Marin Hinata.H..."), you would need to adapt these steps with more detailed information about your data and objectives. # Example data: proportions of people liking a
# Create a binomial distribution for each distributions = [stats.binom(sample_sizes[i], proportions[i]) for i in range(len(proportions))] 0.1]) sample_sizes = np.array([100
