Correlation Analysis Techniques
Relevant source files
The following files were used as context for generating this wiki page:
The Correlation Analysis Techniques module provides a set of statistical tools designed to ingest numerical datasets from external text files and perform descriptive and comparative analysis. This module focuses on quantifying central tendency, dispersion, and distribution characteristics such as skewness and variation coefficients.
The system is architected to handle raw data inputs, specifically biomedical or physiological metrics like cholesterol levels and body weight, and transform them into actionable statistical insights using standard libraries such as numpy and statistics.
Data Ingestion and Processing
The core logic begins with the extraction of numerical data from flat text files. The system implements a robust ingestion pipeline that handles file reading, whitespace stripping, and error handling for non-numeric entries.
File Parsing Logic
The open_file function serves as the primary entry point for data acquisition. It iterates through file lines, ensuring that only valid floating-point numbers are appended to the global data structure.
The flow demonstrates the sequential processing of input data and the exception handling mechanism for malformed data.
Sources: hw1/p1.py:7-17
Statistical Computation Suite
The system calculates several key metrics to describe the dataset. These are categorized into central tendency, variability, and distribution shape (skewness).
Metric Definitions
The following table outlines the statistical metrics computed by the compute_file function:
| Metric | Calculation Method / Library | Description |
|---|---|---|
| Mean | np.mean(data) | The arithmetic average of the dataset. |
| Median | np.median(data) | The middle value in the sorted dataset. |
| Mode | statistics.mode(data) | The most frequently occurring value. |
| Range | np.ptp(data) | Peak-to-peak range (Max - Min). |
| Standard Deviation | np.std(data) | Square root of variance. |
| Variance | np.var(data) | Average of squared deviations from the mean. |
| CV1 (Pearson's) | 3*(mean - median) / std | First Coefficient of Variation for skewness. |
| CV2 (Moment-based) | sum((x-mean)^3) / (N * std^3) | Second Coefficient of Variation for skewness. |
Sources: hw1/p1.py:23-53
Coefficient of Variation Implementation
The implementation distinguishes between two types of variation coefficients to measure distribution asymmetry. The second coefficient (CV2) is calculated using a list comprehension to determine the third moment about the mean.
# 7. Calculate 1st Coefficient of Variation
cv1 = 3*(mean - median) / std
# 8. Calculate 2nd Coefficient of Variation
cv2 = sum([(x - mean) ** 3 for x in data]) / (len(data) * std ** 3)
Sources: hw1/p1.py:47-53
Data Sources and Structure
The analysis techniques are applied to specific datasets represented by .txt files containing column-aligned numerical values.
- CHOLESTEROL.txt: Contains 28 integer observations ranging from 74 to 282. Sources: hw1/CHOLESTEROL.txt:1-29
- WEIGHT.txt: Contains 12 integer observations representing weights such as 62, 51, and 71. Sources: hw1/WEIGHT.txt:1-12
Component Relationship
The interaction between the computation script and the data files is illustrated in the following diagram:
This sequence ensures that the global data list is populated before any mathematical operations are performed.
Sources: hw1/p1.py:19-21, hw1/p1.py:55
Summary
The Correlation Analysis Techniques module facilitates automated statistical profiling of numerical datasets. By integrating standard Python libraries with custom skewness calculations, it provides a comprehensive overview of data distribution, essential for subsequent correlation and trend analysis within the BME533 project framework.