Applied Statistics and Econometrics
Gain practical skills in statistical analysis and econometric modeling, enhancing your ability to interpret data and make informed economic decisions.
Applied Statistics
Comprehensive introduction to statistics covering descriptive and inferential methods, probability, distributions, hypothesis testing, and regression analysis with practical applications and examples.
Практика: Прикладная геостатистика - Часть I
Освойте основы прикладной геостатистики для анализа и интерпретации пространственных данных в геологии и смежных областях.
Основы прикладной геостатистики: Интерполяция, экстраполяция, аппроксимация и вариограммный анализ - Часть 2
Изучите основы прикладной геостатистики, включая интерполяцию, экстраполяцию, аппроксимацию и вариограммный анализ.
Основы прикладной геостатистики
Изучите основы прикладной геостатистики для анализа пространственных данных и моделирования геологических процессов.
Rank Overparameterization and Global Optimality Certification in Low-Rank Matrix Estimation
Explore rank overparameterization in nonconvex optimization, its impact on spurious local minima, and methods for certifying global optimality in large-scale problems.
F Test and Hypothesis Testing in Applied Statistics
Master the F-test concept in statistics through practical numerical examples and hypothesis testing applications, perfect for understanding statistical analysis in engineering and business contexts.
T-Test Numerical Problems in Applied Statistics and Hypothesis Testing
Master the step-by-step procedure for solving t-test problems in statistics, focusing on practical applications and hypothesis testing methods for engineering and business analysis.
Chi Square Test for Independence of Attributes in Applied Statistics - Numerical Hypothesis Testing
Master statistical analysis through Chi-Square testing, focusing on attribute independence and hypothesis testing with practical numerical examples and applications.
Test for Single Proportion and Hypothesis in Applied Statistics
Master statistical hypothesis testing through single proportion analysis, covering one-tail and two-tail tests with practical applications and numerical examples.
Machine Learning for Protein Engineering in Drug Discovery
Discover how machine learning intersects with molecular biology and protein engineering through insights from UC Berkeley Professor Jennifer Listgarten's keynote address on advancing drug discovery innovations.