statscend 0.1.4

A Python package for performing various statistical analyses

StatScend

The statscend package is a Python library designed to provide a convenient and user-friendly interface for performing statistical analysis. Currently, the library includes a set of functions for fitting and evaluating regression models. More functions will be added soon.

The package is designed to be user-friendly, with intuitive syntax and clear output. It is intended for use by researchers, data scientists. Under the hood, statscend utiize the statsmodels and sklearn libraries to provide a range of data analysis and modeling functions. Essentially, this library provides a high-level interface that abstracts away many of the complexities of working with statistical models, making it easy to get started with data analysis in Python.

Installation

You can install Statscent using pip. First, make sure you have Python 3.x and pip installed on your system. Then, open a terminal window and run the following command:

pip install statscend

This will download and install the latest version of the Statscent package and its dependencies. If you prefer to install a specific version of the package, you can specify the version number using pip:

pip install statscent==1.0.0

Once you've installed Statscent, you can import it in your Python code using the following statement:

import statscent

However, since the package name is quite long, you may want to use a shorter alias for convenience. We suggest using st as the alias, since this follows the same naming convention used by other popular data analysis libraries like pandas (pd) and numpy (np).

Here's an example of how to import Statscent with the st alias:

import statscent as st

This will allow you to use the Statscent package in your code using the shorter st alias, like so:

st.linear_regression(data, x y)

While you are free to use any alias you prefer, we believe that using st will help to ensure a smooth and consistent experience for all Statscent users.

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Dependencies

• numpy
• pandas
• statsmodels
• sklearn
• seaborn

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License

This package is licensed under the MIT License.

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Linear Regression

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linear_regression(data, x, y)

This function performs a linear regression analysis on the input dataset.

Parameters

Parameter	Data type	Description
data	pandas DataFrame	The input dataset.
x	string or list of strings	The name(s) of the column(s) to be used as predictor variable(s).
y	string	The name of the column to be used as the response variable.

Returns

This function returns a dictionary of tables that summarize the results of a linear regression analysis. The tables included are

• coefficients table: shows the estimated coefficients and standard errors for each predictor variable,
• summary table: provides an overview of the regression results,
• diagnostics table: includes various diagnostic measures such as the residuals and leverage values.

These tables can be used to evaluate the fit of the regression model and to identify any potential issues such as outliers or multicollinearity.

Exmaple

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Logistic Regression

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logistic_regression(data, x, y)

This function performs a logistic regression analysis on the input dataset.

Parameters

Parameter	Data type	Description
data	pandas DataFrame	The input dataset.
x	string or list of strings	The name(s) of the column(s) to be used as predictor variable(s).
y	string	The name of the column to be used as the response variable.

Returns

This function returns a dictionary of tables that summarize the results of a linear regression analysis. The tables included are

• summary_table: Logistic Regression Summary Table. This table provides information on the model's goodness of fit, including the number of observations, the model's Log-Likelihood value, and the Wald Chi-Square test statistic.

• coefficients_table: provides information on the regression coefficients of the predictors used in the logistic regression model. This table includes columns for the predictor variable name, unstandardized coefficient estimates, standardized coefficient estimates, standard error, z-value, p-value, confidence interval lower bound, confidence interval upper bound, and odds ratio.

• predictive_measures_table: provides information on the predictive measures of the logistic regression model. This table includes columns for the accuracy, specificity, and sensitivity of the model.

• classification_table: provides information on the classification accuracy of the logistic regression model. This table includes the number of true positives, true negatives, false positives, and false negatives, as well as the percentage of correct classifications for each category.