AI-Aquatica

📊 Usage – Statistical Analysis (AI-Aquatica)

This guide describes how to perform basic and advanced statistical analyses using the statistical_analysis module.

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1. 📦 Importing

from ai_aquatica.statistical_analysis import (
    calculate_basic_statistics,
    plot_distribution,
    plot_boxplot,
    calculate_correlation_matrix,
    perform_anova,
    decompose_time_series
)

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2. 📈 Sample dataset

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'NO3': [1.5, 1.7, 1.6, 1.8, 1.4],
    'pH': [7.0, 6.9, 7.1, 7.2, 6.8],
    'Region': ['A', 'A', 'B', 'B', 'A']
})

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3. 🔹 Basic statistics

stats = calculate_basic_statistics(data[['NO3', 'pH']])
print(stats)

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4. 📊 Visualizations

Histogram + KDE

plot_distribution(data, 'NO3')

Boxplot

plot_boxplot(data, 'pH')

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5. 🔗 Correlation matrix

corr_matrix = calculate_correlation_matrix(data[['NO3', 'pH']])
print(corr_matrix)

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6. 📊 ANOVA (Analysis of Variance)

anova_results = perform_anova(data, 'NO3 ~ Region')
print(anova_results)

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7. ⏱️ Time series decomposition

# Requires datetime index and regular time steps
time_data = pd.DataFrame({
    'date': pd.date_range(start='2023-01-01', periods=12, freq='M'),
    'NO3': np.random.rand(12)
}).set_index('date')

decomposition = decompose_time_series(time_data, 'NO3', model='additive', freq=12)

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🧠 Notes

• ANOVA requires categorical variables in the formula (e.g., ‘pH ~ Region’).
• Time series decomposition needs a datetime index and consistent frequency.
• Visuals are shown using matplotlib and seaborn.

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