Homework 1 Module Overview
Relevant source files
The following files were used as context for generating this wiki page:
Introduction
The Homework 1 module provides a foundational framework for basic statistical analysis of biomedical datasets. It is designed to ingest numerical data from external text files and perform a series of descriptive statistical calculations, including central tendency measures and dispersion metrics.
This module primarily focuses on automated data parsing and the application of mathematical formulas to datasets such as cholesterol levels and body weight. It utilizes standard Python libraries like numpy and statistics to ensure accuracy in computational results.
Sources: hw1/p1.py:1-6, hw1/CHOLESTEROL.txt
Data Ingestion and Processing
The module implements a linear data flow starting from raw text files to a processed list of floating-point numbers. The system handles potential data quality issues by implementing basic error handling for non-numeric entries.
File Parsing Logic
The function open_file(file_name) is responsible for reading data. It iterates through each line of the provided file, strips whitespace, and attempts to convert the content into a float. If a ValueError occurs, the system logs the issue and skips the specific line.
This diagram illustrates the iterative parsing and validation process used during data ingestion.
Sources: hw1/p1.py:7-17
Statistical Calculations
The compute_file function orchestrates the calculation of several statistical metrics. It populates a global data list and then applies various mathematical operations to derive descriptive statistics.
Metric Summary Table
| Metric | Implementation Method | Description |
|---|---|---|
| Mean | np.mean(data) | 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 difference (Max - Min). |
| Std Deviation | np.std(data) | Measure of variation or dispersion. |
| Variance | np.var(data) | The square of the standard deviation. |
Sources: hw1/p1.py:23-44
Advanced Coefficients
In addition to standard metrics, the module calculates specific coefficients of variation to analyze the distribution's skewness and relative variability.
- First Coefficient of Variation (Pearson's Skewness): Calculated as
3 * (mean - median) / std. - Second Coefficient of Variation: Calculated using a third-moment formula:
sum([(x - mean) ** 3 for x in data]) / (len(data) * std ** 3).
Sources: hw1/p1.py:46-51
Execution Sequence
The module follows a sequential execution pattern where the file name is passed to a high-level wrapper, which then triggers the I/O and calculation stages.
This sequence shows the interaction between the core functions and external libraries.
Sources: hw1/p1.py:19-53
Dataset Specifications
The module is tested against specific input datasets provided in .txt format. Each file contains a single column of numeric values representing different biological or physical measurements.
- CHOLESTEROL.txt: Contains 28 data points (e.g., 205, 166, 236) reflecting cholesterol measurements.
- WEIGHT.txt: Contains 12 data points (e.g., 62, 51, 56) representing body weight measurements.
Sources: hw1/CHOLESTEROL.txt:1-29, hw1/WEIGHT.txt:1-12
Summary
The Homework 1 module establishes a reliable pipeline for reading biomedical data and extracting essential statistical insights. By combining the numpy library for optimized calculations with custom coefficient formulas, it provides a comprehensive overview of dataset characteristics, including central tendency, dispersion, and skewness.
Sources: hw1/p1.py