MarkHmnv / staty

The Staty library provides functions for calculating statistical measures on a dataset and applying basic Machine Learning techniques.

Staty

The Staty library provides functions to calculate statistical measures on a dataset. It runs on Python 3. This library is a personal pet project created for the purpose of learning Data Science and Statistics concepts.

Usage

Here is an example:

import staty.core as staty

data = [2, 4, 6, 8]
print(staty.stderr(data))  # 1.2909944487358056

Functions

Staty provides the following functions:

1. mean(data: List[Union[int, float]]) -> float

   • Calculates the mean of a list of numbers.

2. var(data: List[Union[int, float]], is_sample: bool = True) -> float

   • Calculates the variance of a given dataset.

3. stdev(data: List[Union[int, float]], is_sample: bool = True) -> float

   • Calculates the standard deviation of a given list of numbers.

4. stderr(data: List[Union[int, float]], is_sample: bool = True) -> float

   • Calculates the standard error of a data set.

5. median(data: List[Union[int, float, str]]) -> Union[float, Tuple[float, float]]

   • Calculates the median value(s) of the given list of data.

6. mode(data: List[Union[int, float, str]]) -> Union[float, str, List]

   • Finds the mode(s) of a given list of data.

7. cv(data: List[Union[int, float]], is_sample: bool = True) -> float

   • Calculates the coefficient of variation for a given list of data.

8. cov(data_x: List[Union[int, float]], data_y: List[Union[int, float]], is_sample: bool = True) -> float

   • Calculates the covariance between two sets of data points.

9. correlation_r(data_x: List[Union[int, float]], data_y: List[Union[int, float]], is_sample: bool = True) -> float

   • Calculates the Pearson correlation coefficient between two sets of data.

10. zscore(data: List[Union[int, float]], is_sample: bool = True) -> List[float]

    • Calculate the z-scores of a list of data points.

11. tscore(data: List[Union[int, float]]) -> List[float]

    • Calculates the t-scores of a list of data points.

12. z_interval(data: List[Union[int, float]], confidence_lvl: float) -> Tuple[float, float]

    • Calculate the z-test confidence interval of a list of data points.

13. z_interval_equal_var(data_x: List[Union[int, float]], data_y: List[Union[int, float]], confidence_lvl: float) -> Tuple[float, float]

    • Calculate the z-test confidence interval for two samples, assuming the population variance is equal.

14. z_test(data: List[Union[int, float]], expected: float, two_tailed: bool, significance_lvl: float, direction: int) -> Tuple[bool, float]

    • Perform a z-test to determine if the sample mean is significantly different from the expected value.

15. t_interval(data: List[Union[int, float]], confidence_lvl: float ) -> Tuple[float, float]

    • Calculate the t confidence interval of a list of data points.

16. t_interval_equal_var(data_x: List[Union[int, float]], data_y: List[Union[int, float]], confidence_lvl: float ) -> Tuple[float, float]

    • Calculate the t confidence interval for two samples, assuming the population variance is equal.

17. t_test(data: List[Union[int, float]], expected: float, two_tailed: bool, significance_lvl: float, direction: int) -> Tuple[bool, float]

    • Perform a t-test to determine if the sample mean is significantly different from the expected value.