Lectures related to Model Selection in Statistics

Statistical Measures: Mean, Median, and Dispersion Techniques

Discusses statistical measures of central tendency and dispersion, focusing on mean, median, and their implications in data analysis.

Statistics: Exploratory Data Analysis

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Central Tendency and Dispersion

Explores replicates, visualization methods, central tendency measures, outliers, dispersion, averages, residuals, and unbiased estimators.

Statistics: Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Measures of central tendency

Covers mean, median, mode, box plots, and histograms in datasets.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Statistics and visualisation of morphometric data

Covers statistical analysis and visualization of morphometric data, including discrimination between tree types and numpy calculations.

Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Exploratory Statistics: Understanding Populations and Samples

Covers exploratory statistics, focusing on populations, samples, and various statistical measures.

Numpy: Broadcasting, Operations, Comparisons, and Constants

Covers broadcasting, operations, comparisons, and numpy constants like pi, e, and infinity.

Vectors of Random Variables: Empirical Distributions

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Applications of Quantum Science: Densities and Statistics

Covers the applications of densities and statistics in quantum science, focusing on binomial and Poisson distributions.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Statistics: Random Variables and Probability Density Functions

Introduces random variables, probability density functions, and Gaussian distribution in statistics.

Probability Theory: Central Limit Theorem

Explores probability theory, distribution of averages, and the central limit theorem.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.