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        "description": "# *Nightingale*\n\nI named this package *Nightingale* in honour of \n[Florence Nightingale](https://en.wikipedia.org/wiki/Florence_Nightingale), \n[The lady with the data](https://thisisstatistics.org/florence-nightingale-the-lady-with-the-data/).\n\n# Installation\n\nYou can use pip to install Nightingale:\n\n```bash\npip install nightingale\n```\n\n# Usage\n\n## Population Proportion\n\n```python\nfrom nightingale import get_sample_size, PopulationProportion, get_z_score\n\nprint('z-score for 0.95 confidence:', get_z_score(confidence=0.95))\nprint('sample size:', get_sample_size(confidence=0.95, error_margin=0.05, population_size=1000))\nprint('with 10% group proportion:', get_sample_size(confidence=0.95, error_margin=0.05, population_size=1000, group_proportion=0.1))\n\npopulation_proportion = PopulationProportion(sample_n=239, group_proportion=0.5)\nprint('error:', population_proportion.get_error(confidence=0.95))\n```\n\n## Ordinary Least Squares (OLS)\n\n```python\nimport pandas as pd\nimport numpy as np\nfrom nightingale import OrdinaryLeastSquares\n\ndata = pd.DataFrame({\n    'x': np.random.normal(size=20, scale=5), \n    'y': np.random.normal(size=20, scale=5),\n})\ndata['z'] = data['x'].values + data['y'].values + np.random.normal(size=20, scale=1)\nprint('data:')\ndisplay(data.head())\n\nols = OrdinaryLeastSquares(data=data, formula='z ~ x + y')\nprint('ols results:')\ndisplay(ols.table)\n\nprint('r-squared:', ols.r_squared)\nprint('adjusted r-squared:', ols.adjusted_r_squared)\n\nprint('\\n', 'summary:')\ndisplay(ols.summary)\n```\n\n## *ANOVA*\n\n\n# References\nz-score: https://stackoverflow.com/questions/20864847/probability-to-z-score-and-vice-versa-in-python\n\n\n\n",
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