Hypothesis testing is a fundamental concept in statistics, used to test whether a certain claim about a population is likely to be true. In Python, the SciPy library provides a range of functions for hypothesis testing, including t-tests, ANOVA, and chi-squared tests.
To get started with hypothesis testing in Python, you can refer to the official SciPy documentation: **https://docs.scipy.org/doc/scipy/reference/stats.html**
Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In Python, the scikit-learn library provides a range of functions for regression analysis, including linear regression, logistic regression, and polynomial regression.
To get started with regression analysis in Python, you can refer to the official scikit-learn documentation: **https://scikit-learn.org/stable/modules/linear_model.html**
Probability theory is a branch of mathematics that deals with the analysis of random phenomena. In Python, the NumPy library provides a range of functions for probability theory, including probability distributions, random number generation, and statistical functions.
To get started with probability theory in Python, you can refer to the official NumPy documentation: **https://numpy.org/doc/stable/reference/random/index.html**
Pandas is a popular Python library for data manipulation and analysis. It provides a range of functions for handling structured data, including data cleaning, data transformation, and data aggregation. Pandas is particularly useful for working with tabular data, such as data stored in spreadsheets or databases.
To get started with Pandas, you can refer to the official Pandas documentation: **https://pandas.pydata.org/docs/**
Numpy is a fundamental library for scientific computing in Python. It provides a range of functions for numerical operations, including linear algebra, Fourier transforms, and random number generation. Numpy is particularly useful for working with numerical data, such as data from sensors or simulations.
To get started with Numpy, you can refer to the official Numpy documentation: **https://numpy.org/doc/stable/**