Concept
Pearson's chi-squared test
Related lectures (54)
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Two-way ANOVA and Chi-square Test
Explores two-way ANOVA, chi-square test, model comparison, and variable transformation for statistical analysis.
Estimating Maximum Streamflow with LP3 Distribution
Covers the frequency factor approach and the log-Pearson Type III distribution for estimating maximum streamflow.
Multilinear Regression: Least Square Fit
Covers multilinear regression using least square fit method and the importance of standardizing variables.
Chi-Square Test: Independence Hypothesis
Explains the Chi-Square test for independence hypothesis and its practical applications.
Markov Chains: Properties and Approximations
Explores Markov chains' properties, hitting time, and Stirling's formula approximation.
Continuous Distributions: Testing and Estimation
Explores testing and estimating continuous distributions through interval partitioning and expected number calculations.
Gaussian Random Vectors
Explores Gaussian vectors, moment generating functions, independence, density functions, affine transformations, and quadratic forms.
Chi-squared Test: Hypothesis Testing
Introduces the chi-squared test for hypothesis testing against theoretical distributions, showcasing its application with dice fairness testing.
Confidence Intervals and Hypothesis Tests
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.