Lectures related to Categorical variable

Experimental Design and Analysis

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Goodness-of-Fit Test: Variables and Distributions

Covers the revision of variables, categorical data analysis, and goodness-of-fit tests.

Decision Tree Classification

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Categorical Data Analysis: Poisson GLM

Explores Poisson GLM for analyzing count data in categorical analysis, focusing on contingency tables and Bayesian inference.

Probability and Statistics

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Panel data: dynamic model

Explores the dynamic model for panel data analysis and the Markov assumption.

Thematic attributes and classification

Covers statistical thematic mapping, types of thematic maps, and discretization methods for representing and classifying dataset characteristics.

Exploratory Statistics: Data Analysis Fundamentals

Covers the basics of exploratory statistics, including variables, quantiles, central tendency, dispersion, outliers, and robustness.

Thematic Attributes and Classification

Covers statistical thematic mapping, types of maps, discretization methods, and proportional symbols in maps.

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Experimental Design: Variables and Hypotheses

Covers research questions, hypotheses, variables, and experimental designs in learning experiments.

Exploratory Statistics: Understanding Populations and Samples

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

Visualizing Categorical Data: Methods and Examples

Explores tools for visualizing categorical data to detect patterns and trends.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Linear Regression Essentials

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

Linear Regression: Simple

Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.

Statistical Inference: Concepts and Applications

Covers statistical inference concepts, emphasizing data-probability connection, types of variables, and data analysis phases.

Planetary Boundaries: Novel Entities

Delves into planetary boundaries, emphasizing novel entities' impact and the need for precautionary control variables.

Categorical Data Analysis: Methods and Examples

Explores the analysis of categorical and discrete data with examples.