Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Concept
Margin of error
Formal sciences
Statistics
Statistical inference
Mathematical statistics
Graph Chatbot
Related lectures (32)
Login to filter by course
Login to filter by course
Reset
Python Programming: File Handling and Exceptions
Explores file handling and exceptions in Python programming, covering reading, writing, and error handling strategies.
Statistical Estimation: Properties and Distributions
Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.
Model Selection: Generalization and Validation
Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.
Convergence of Fixed Point Methods
Explores the convergence of fixed point methods and the implications of different convergence rates.
Practical Engineering 1: Signal Processing
Explores advanced software engineering topics and signal processing using lazy lists to build a sound synthesizer from scratch.
Fourier Transform: Properties and Convolution
Covers the properties and convolution of the Fourier transform.
Measurement Principles: Calibration and Sensitivity Examples
Discusses measurement principles through examples of calibration, sensitivity, and optical measurement techniques.
Significant Figures, Error Estimation, Notation
Covers significant figures, notation for derivatives, and error estimation methods.
Statistical Tests: T-Tests and ANOVA
Covers the calculation of paired t-tests, advantages/disadvantages of different t-tests, and the concept of ANOVA.
ANOVA: Partitioning Total SS
Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
Conjugate Gradient Method: Iterative Optimization
Covers the conjugate gradient method, stopping criteria, and convergence properties in iterative optimization.
Derivation of the logit model
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
Previous
Page 2 of 2
Next
