Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Statistical Inference: Weighted Mean and Radioactive Decay
Graph Chatbot
Related lectures (49)
Generalized Linear Models: A Brief Review
Log in to Mediaspace to watch this video
Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Statistical Inference: Confidence Intervals
Log in to Mediaspace to watch this video
Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.
Conditional Probability: Definition and Examples
Log in to Mediaspace to watch this video
Explains the calculation of conditional probability with definitions and examples.
Applications of Quantum Science: Densities and Statistics
Log in to Mediaspace to watch this video
Covers the applications of densities and statistics in quantum science, focusing on binomial and Poisson distributions.
ECDF and Quantiles
Log in to Mediaspace to watch this video
Covers ECDF, quantiles, mean, median, and box plots for dataset analysis.
Statistical Models: Sampling and Hypothesis Testing
Log in to Mediaspace to watch this video
Explores statistical models, sampling distributions, and hypothesis testing using real-world examples.
Point Estimation in Statistics
Log in to Mediaspace to watch this video
Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.
Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods
Log in to Mediaspace to watch this video
Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.
Conditional Probability: Understanding Events and Their Relationships
Log in to Mediaspace to watch this video
Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.
L-Moment Estimation: Probability-Weighted Moments
Log in to Mediaspace to watch this video
Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.
Statistical Theory: Decision Theory Framework
Log in to Mediaspace to watch this video
Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.
Regression Methods: Model Building and Inference
Log in to Mediaspace to watch this video
Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.
Damped Harmonic Oscillator
Log in to Mediaspace to watch this video
Covers the damped harmonic oscillator in quantum optics, discussing alternative descriptions and extensions like finite temperature.
Stochastic Models for Communications: Continuous-Time Stochastic Processes - Stationarity
Log in to Mediaspace to watch this video
Covers the concept of stationarity in continuous-time stochastic processes and its implications in communication systems.
Statistics for Civil Engineering
Log in to Mediaspace to watch this video
Covers the fundamentals of statistics, exploring probabilistic models, variability, uncertainty, and data collection.
Statistical Models and Parameter Estimation
Log in to Mediaspace to watch this video
Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.
Untitled
Log in to Mediaspace to watch this video
Logistic Regression: Probability Modeling
Log in to Mediaspace to watch this video
Covers logistic regression for binary classification using probability modeling and optimization methods.
Neutrino Mass Measurements
Log in to Mediaspace to watch this video
Explores the measurement of neutrino masses in beta decays and direct measurements of electron, muon, and tau neutrino masses.
Maximum Likelihood Estimation: Theory
Log in to Mediaspace to watch this video
Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
Previous
Page 2 of 3
Next