Lectures related to Standard score

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Real Estate Market Trends

Explores the evolution of real estate prices, hedonic models, and the UBS Swiss Real Estate Bubble Index.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Normal Distribution: Characteristics and Z-scores

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Modeling Elasticity and Coefficients

Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Multilinear Regression: Standardized Variables and Effects

Explores standardizing variables and effects in multilinear regression analysis.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Parameter Estimation & Fisher Information

Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.

Evolution of National Economy Representation

Explores the evolution of GDP calculation and harmonization across European countries.

PCA: Directions of Largest Variance

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Confidence Interval for Bernoulli Model Score

Explains how to calculate the confidence interval for the score parameter in the Bernoulli model.

Confidence Intervals and T-Test

Explores confidence intervals, T-test, and hypothesis testing, including assumptions and critical regions.

Data Exploration: Normal Distribution

Explores data distribution strategies, normal modeling, and checking normality with graphical tools.

Normal Distributions

Covers the characteristics and importance of the normal distribution in statistical analysis.

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Statistical Hypothesis Testing: Basics and Applications

Covers the basics of statistical hypothesis testing, p-values, confidence levels, and t-tests.

Fisher Information, Cramér-Rao Inequality, MLE

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Linear Binary Classification

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.