May 1, 2024
3 minute read
Chi-squared test, also known as the Pearson's chi-squared test, is a statistical test used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. It is widely used in various fields, including data analysis, research, and quality control, to assess the association between two or more variables.
Understanding Chi-squared Test
The chi-squared test is based on the chi-squared distribution, which is a probability distribution that describes the distribution of the squared differences between expected and observed frequencies. The test statistic, denoted as χ², is calculated by summing the squared differences between the observed and expected frequencies for each category and dividing the result by the expected frequency.
The chi-squared statistic follows a chi-squared distribution with degrees of freedom equal to the number of categories minus one. The degrees of freedom determine the shape of the distribution and the critical value for statistical significance.
Applications of Chi-squared Test
The chi-squared test has a wide range of applications in various fields:
b3zs1r|
Find a path to becoming a Chi-squared Test. Learn more at:
OpenCourser.com/topic/b3zs1r/chi
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Chi-squared Test.
Provides comprehensive coverage of statistical learning methods, including supervised and unsupervised learning, regression, classification, and clustering. It is appropriate for advanced courses in statistics and machine learning.
Provides a comprehensive introduction to deep learning, which type of machine learning that uses neural networks to learn from data. It covers topics such as deep learning architectures, training methods, and applications.
This classic text provides a comprehensive and rigorous introduction to statistical methods used in research. It covers topics such as descriptive statistics, probability theory, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to causal inference, which is the process of drawing conclusions about causal relationships from observational data. It covers topics such as graphical models, counterfactuals, and causal effects.
Provides a practical introduction to Bayesian statistics. It covers topics such as probability distributions, Bayesian inference, and Markov chain Monte Carlo (MCMC) methods.
Provides a comprehensive introduction to time series analysis, which is the study of data that is collected over time. It covers topics such as time series models, forecasting, and spectral analysis.
Provides a comprehensive introduction to data mining, which is the process of extracting knowledge from large amounts of data. It covers topics such as data preprocessing, data mining algorithms, and data visualization.
Covers the fundamental concepts, methods, and applications of the chi-squared test. It provides clear explanations and examples, making it suitable for both beginners and advanced users.
Is designed for survey statisticians. It provides advanced techniques for chi-squared tests in the context of survey sampling and data analysis.
Provides a practical introduction to machine learning, which is the process of training computers to learn from data. It covers topics such as supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to statistical power analysis, which is the process of determining the minimum sample size needed to achieve a desired level of statistical significance. It covers topics such as effect size, power analysis, and sample size determination.
Provides a comprehensive introduction to nonparametric statistics, which are statistical methods that do not require the assumption of a normal distribution. It covers topics such as hypothesis testing, confidence intervals, and regression analysis.
Provides a foundation in applied statistics and includes material on sampling distributions, estimation, hypothesis testing, regression, and analysis of variance. It is appropriate for both introductory and advanced courses in statistics.
Provides a comprehensive introduction to statistical methods used in psychology. It covers topics such as descriptive statistics, probability theory, hypothesis testing, and regression analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/b3zs1r/chi
Save
