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Statistical Analysis

Perform comprehensive statistical analysis on datasets including descriptive statistics, hypothesis testing, correlation analysis, and predictive modeling.

Statistical Analysis

Perform comprehensive statistical analysis on datasets within the sandbox environment including descriptive statistics, hypothesis testing, correlation analysis, regression modeling, and advanced statistical computations.

📊 Advanced Statistical Computing

Statistical analysis provides powerful analytical capabilities including parametric and non-parametric tests, multivariate analysis, time series analysis, and machine learning algorithms for data insights.

Overview

The Statistical Analysis tool enables comprehensive statistical computing and data analysis within the sandbox environment, supporting descriptive statistics, inferential statistics, predictive modeling, and advanced analytical techniques for data-driven insights.

Key Features

  • Descriptive Statistics - Comprehensive summary statistics and data distribution analysis
  • Hypothesis Testing - Parametric and non-parametric statistical tests
  • Correlation Analysis - Correlation matrices and relationship analysis
  • Regression Modeling - Linear and non-linear regression analysis
  • Predictive Analytics - Machine learning and forecasting capabilities

Methods

statisticalAnalysis

Perform statistical analysis on datasets.

ParameterTypeRequiredDescription
dataPathStringYesPath to dataset file (CSV, XLSX, JSON)
analysisTypeStringNoAnalysis type: 'descriptive', 'inferential', 'predictive', 'comprehensive' (default: 'comprehensive')
targetVariableStringNoTarget variable for analysis (column name)
independentVariablesArrayNoList of independent variables for analysis
statisticalTestsArrayNoSpecific statistical tests to perform
confidenceLevelNumberNoConfidence level for statistical tests (default: 0.95)
outputFormatStringNoOutput format: 'summary', 'detailed', 'report' (default: 'detailed')
{
  "dataPath": "/sandbox/data/survey_results.csv",
  "analysisType": "comprehensive",
  "targetVariable": "satisfaction_score",
  "independentVariables": ["age", "income", "education_level"],
  "statisticalTests": ["t_test", "correlation", "regression"],
  "confidenceLevel": 0.95
}

Output:

  • success (Boolean) - Analysis operation success status
  • dataPath (String) - Path to analyzed dataset
  • descriptiveStats (Object) - Descriptive statistics summary
  • inferentialStats (Object) - Hypothesis testing results
  • correlationAnalysis (Object) - Correlation analysis results
  • regressionAnalysis (Object) - Regression modeling results
  • predictiveModels (Object) - Predictive model results
  • visualizations (Object) - Generated charts and plots
  • analysisTime (Number) - Analysis duration in milliseconds

Descriptive Statistics

Summary Statistics and Data Distribution

Hypothesis Testing

Statistical Tests and Inference

Regression Analysis

Linear and Non-Linear Modeling

Error Handling

Common Statistical Analysis Issues

Error TypeCauseResolution
Data Format ErrorIncompatible data formatVerify CSV/XLSX format and structure
Missing VariableSpecified variable not foundCheck column names and data structure
Insufficient DataToo few observations for analysisIncrease sample size or use simpler tests
Assumption ViolationsStatistical assumptions not metUse non-parametric alternatives
Convergence ErrorModel fails to convergeAdjust parameters or preprocessing

Robust Statistical Analysis

def robust_statistical_analysis(data_path, target_variable, predictor_variables, fallback_methods=None):
    """Perform statistical analysis with comprehensive error handling and fallbacks."""
    
    if not fallback_methods:
        fallback_methods = [
            {"analysisType": "comprehensive", "statisticalTests": ["correlation", "regression", "t_test"]},
            {"analysisType": "descriptive", "statisticalTests": ["correlation"]},
            {"analysisType": "descriptive", "statisticalTests": ["basic_stats"]}
        ]
    
    for i, method in enumerate(fallback_methods):
        try:
            print(f"🔄 Attempting analysis method {i+1}")
            
            analysis_params = {
                "dataPath": data_path,
                "targetVariable": target_variable,
                "independentVariables": predictor_variables,
                **method
            }
            
            result = statisticalAnalysis(analysis_params)
            
            if result['success']:
                print(f"✅ Analysis successful with method {i+1}")
                return {
                    "success": True,
                    "method_used": i+1,
                    "method_details": method,
                    "results": result
                }
            else:
                print(f"⚠️ Method {i+1} failed: {result.get('error')}")
        
        except Exception as e:
            print(f"💥 Method {i+1} exception: {str(e)}")
    
    return {
        "success": False,
        "error": "All analysis methods failed",
        "methods_attempted": len(fallback_methods)
    }

# Usage with error handling
robust_result = robust_statistical_analysis("/sandbox/problematic/incomplete_data.csv", 
                                          "outcome", ["predictor1", "predictor2"])

XLSX Analysis

Analyze Excel spreadsheets and extract data for statistical analysis

Data Transformation

Transform and preprocess data for statistical analysis

Read File

Read datasets for statistical analysis from various formats

Next Steps: Combine with Data Transformation for data preprocessing, or use XLSX Analysis for Excel-specific analysis.