Sample size determination

Related lectures (31)

Page 3 of 4

Explores the Gauss-Markov Theorem and the optimality of Least Squares Estimators in the Gaussian Linear model.

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Explores sampling, inferential statistics, and effective experimentation in statistics.

Explores ceramic forming methods, emphasizing particle packing, compaction, additives, ejection, and slip casting practical aspects.

Explores the motivation and ideas behind convolutional networks, emphasizing weight sharing and pooling layers.

Introduces basic bounds, LLN, and CLT in probability theory, emphasizing convergence to normal distribution.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Covers statistical mechanical resistance in ceramics, including Weibull statistic and stable cracking behavior in compression.