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Performs comprehensive statistical analysis on grouped biological/experimental data, including descriptive statistics, correlation analysis, ANOVA testing, and visualization of infection levels and growth performance across different groups.

Purpose

This function is designed to analyze experimental or biological datasets with multiple groups, specifically examining relationships between infection levels and growth performance. It provides statistical summaries, visual comparisons via boxplots, correlation analysis using Pearson's method, and ANOVA testing to determine significant differences between groups. The function identifies the best performing group and provides interpretable conclusions about statistical significance.

Source Code

def perform_analysis(data):
    try:
        # Display the first few rows of the dataset
        print(data.head())

        # Descriptive statistics
        print("\nDescriptive Statistics:")
        print(data.describe())

        # Assuming 'group' is the column indicating different groups
        # and 'infection_level' and 'growth_performance' are columns of interest
        group_column = 'group'
        infection_column = 'infection_level'
        performance_column = 'growth_performance'

        # Check if necessary columns exist
        if not all(col in data.columns for col in [group_column, infection_column, performance_column]):
            print("Error: One or more required columns are missing from the dataset.")
            return

        # Group-wise descriptive statistics
        grouped_data = data.groupby(group_column)
        print("\nGroup-wise Descriptive Statistics:")
        print(grouped_data[[infection_column, performance_column]].describe())

        # Visualize the data
        sns.boxplot(x=group_column, y=infection_column, data=data)
        plt.title('Infection Level by Group')
        plt.show()

        sns.boxplot(x=group_column, y=performance_column, data=data)
        plt.title('Growth Performance by Group')
        plt.show()

        # Correlation analysis
        correlation, p_value = stats.pearsonr(data[infection_column], data[performance_column])
        print(f"\nCorrelation between infection level and growth performance: {correlation:.2f}")
        print(f"P-value: {p_value:.4f}")

        # Group comparison using ANOVA
        anova_results = sm.stats.anova_lm(sm.OLS(data[performance_column], sm.add_constant(pd.get_dummies(data[group_column]))).fit(), typ=2)
        print("\nANOVA Results:")
        print(anova_results)

        # Conclusion based on ANOVA and correlation
        if anova_results['PR(>F)'][0] < 0.05:
            print("\nThere is a significant difference in growth performance between groups.")
        else:
            print("\nThere is no significant difference in growth performance between groups.")

        if p_value < 0.05:
            print("There is a significant correlation between infection level and growth performance.")
        else:
            print("There is no significant correlation between infection level and growth performance.")

        # Identify the best performing group
        best_group = grouped_data[performance_column].mean().idxmax()
        print(f"\nThe best performing group is: {best_group}")

    except Exception as e:
        print(f"An error occurred during analysis: {e}")

Parameters

Parameter Details

data: A pandas DataFrame containing experimental data with required columns: 'group' (categorical variable indicating different experimental groups), 'infection_level' (numeric variable measuring infection severity), and 'growth_performance' (numeric variable measuring growth outcomes). The DataFrame should have at least these three columns with appropriate data types for statistical analysis.

Return Value

This function returns None. It produces side effects including: printing descriptive statistics, group-wise summaries, correlation coefficients, ANOVA results, and conclusions to stdout; displaying two matplotlib boxplot visualizations (infection level by group and growth performance by group). If required columns are missing or an error occurs, it prints an error message and returns early.

Dependencies

• pandas
• numpy
• scipy
• statsmodels
• matplotlib
• seaborn

Required Imports

import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns

Usage Example

import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns

# Create sample data
data = pd.DataFrame({
    'group': ['Control', 'Control', 'Control', 'Treatment_A', 'Treatment_A', 'Treatment_A', 'Treatment_B', 'Treatment_B', 'Treatment_B'],
    'infection_level': [2.5, 3.1, 2.8, 5.2, 4.9, 5.5, 1.2, 1.5, 1.3],
    'growth_performance': [8.5, 8.2, 8.7, 6.1, 6.5, 5.9, 9.2, 9.5, 9.1]
})

# Perform analysis
perform_analysis(data)

# Output will include:
# - First few rows of data
# - Descriptive statistics
# - Group-wise statistics
# - Two boxplot visualizations
# - Correlation analysis results
# - ANOVA results
# - Statistical conclusions
# - Best performing group identification

Best Practices

• Ensure the input DataFrame contains exactly the required column names: 'group', 'infection_level', and 'growth_performance'
• Verify that numeric columns (infection_level, growth_performance) contain valid numeric data without excessive missing values
• The function displays plots using plt.show(), which may block execution in some environments; consider modifying for batch processing
• ANOVA assumes normality and homogeneity of variance; consider checking these assumptions before interpreting results
• The function prints results rather than returning them; consider capturing stdout or modifying to return a results dictionary for programmatic use
• Handle the try-except block output appropriately as errors are only printed, not raised
• Ensure sufficient sample size in each group for meaningful statistical analysis (typically n≥3 per group minimum)
• The function modifies global matplotlib state with plt.show(); consider using figure objects for better control in production code

Tags

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