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RSK World
statsmodels-statistical
RSK World
statsmodels-statistical
Statistical Modeling with Statsmodels
statsmodels-statistical
  • __pycache__
  • data
  • examples
  • notebooks
  • .gitignore458 B
  • CHANGELOG.md4 KB
  • FEATURES.md6.3 KB
  • LICENSE1.2 KB
  • PROJECT_INFO.md2.2 KB
  • PROJECT_SUMMARY.md4.2 KB
  • README.md7.4 KB
  • RELEASE_NOTES_v1.0.0.md6.5 KB
  • UNIQUE_FEATURES.md5.3 KB
  • advanced_time_series.py9.8 KB
  • automated_reporting.py8.3 KB
  • bayesian_statistics.py7.5 KB
  • data_preprocessing.py8.2 KB
  • econometric_modeling.py9.8 KB
  • hypothesis_testing.py12.5 KB
  • index.html10.8 KB
  • model_evaluation.py9.1 KB
  • model_persistence.py6.5 KB
  • model_selection.py9.7 KB
  • panel_data_analysis.py7.3 KB
  • performance_benchmarking.py7.3 KB
  • regression_analysis.py9 KB
  • requirements.txt361 B
  • statistical_diagnostics.py13.8 KB
  • statsmodels-statistical.png284 B
  • time_series_analysis.py10.3 KB
  • visualization_utils.py8.9 KB
README.md.gitignore
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README.md

# Data Files

<!--
Author: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
-->

This directory contains sample datasets for various statistical analyses.

## Available Datasets

### 1. sample_data.csv
**Purpose**: Linear regression analysis
**Columns**:
- `X1`, `X2`, `X3`: Independent variables
- `y`: Dependent variable

**Usage**:
```python
import pandas as pd
df = pd.read_csv('data/sample_data.csv')
X = df[['X1', 'X2', 'X3']].values
y = df['y'].values
```

### 2. time_series_data.csv
**Purpose**: Time series analysis and forecasting
**Columns**:
- `date`: Date index
- `value`: Time series values

**Usage**:
```python
import pandas as pd
df = pd.read_csv('data/time_series_data.csv', parse_dates=['date'], index_col='date')
ts_data = df['value']
```

### 3. panel_data.csv
**Purpose**: Panel data analysis (fixed/random effects)
**Columns**:
- `entity`: Entity identifier
- `time`: Time period
- `X1`, `X2`: Independent variables
- `y`: Dependent variable

**Usage**:
```python
import pandas as pd
from panel_data_analysis import PanelDataAnalysis

df = pd.read_csv('data/panel_data.csv')
panel = PanelDataAnalysis()
panel.prepare_panel_data(df, 'entity', 'time', ['X1', 'X2', 'y'])
```

### 4. hypothesis_test_data.csv
**Purpose**: Hypothesis testing (t-tests, ANOVA)
**Columns**:
- `group`: Group identifier (A, B, C, D)
- `value`: Measurement values

**Usage**:
```python
import pandas as pd
df = pd.read_csv('data/hypothesis_test_data.csv')
groups = df['group'].unique()
samples = {group: df[df['group'] == group]['value'].values for group in groups}
```

### 5. econometric_data.csv
**Purpose**: Econometric modeling (VAR, cointegration)
**Columns**:
- `date`: Date index
- `GDP`: Gross Domestic Product
- `Consumption`: Consumption
- `Investment`: Investment
- `Unemployment`: Unemployment rate

**Usage**:
```python
import pandas as pd
df = pd.read_csv('data/econometric_data.csv', parse_dates=['date'], index_col='date')
data = df[['GDP', 'Consumption', 'Investment']]
```

## Data Characteristics

| Dataset | Rows | Columns | Type |
|---------|------|---------|------|
| sample_data.csv | 30 | 4 | Cross-sectional |
| time_series_data.csv | 90 | 2 | Time series |
| panel_data.csv | 50 | 5 | Panel |
| hypothesis_test_data.csv | 40 | 2 | Cross-sectional |
| econometric_data.csv | 36 | 5 | Time series |

## Notes

- All datasets are synthetic and created for demonstration purposes
- Data files are compatible with pandas DataFrame operations
- Date columns are formatted for easy parsing
- All datasets include author information in comments

## Author

**RSK World**
- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277

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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
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lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
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*.egg

# Virtual Environment
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ENV/
.venv

# Jupyter Notebook
.ipynb_checkpoints
*.ipynb_checkpoints/

# IDE
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*.swp
*.swo
*~

# OS
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# Project specific
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