Comparisons - Samenvattingen en Aantekeningen

This document provides a detailed breakdown of ANOVA, hypothesis testing, and post-hoc comparisons, focusing on: 1. Understanding ANOVA ️ When to use ANOVA (Comparing >2 Means in Different Groups) ️ How ANOVA Works (Between-Group vs. Within-Group Differences) ️ F-Test for Comparing Group Means & Interpreting F-Values 2. Hypothesis Testing & Effect Size ️ Null & Alternative Hypotheses for ANOVA ️ Explained Variation Using Eta Squared (η²) – Like R² in Regression ️ ...

This document provides a detailed breakdown of Factorial ANOVA (Two-Way ANOVA), main effects, and interaction effects, focusing on: 1. Understanding Factorial ANOVA (Two-Way ANOVA) ️ When & why to use Two-Way ANOVA instead of One-Way ANOVA ️ Main effects vs. Interaction effects (Differences in Differences Analysis) ️ Interpreting interaction plots & cell means 2. Hypothesis Testing & Effect Size ️ Omnibus F-Test for Main & Interaction Effects ️ Sum of Squares for Factor A, F...

This document provides a detailed breakdown of partial effects in multiple regression, focusing on: 1. Understanding Partial Effects in Multiple Regression ️ Why b-coefficients alone can’t determine effect strength ️ Standardized Regression Coefficients (b)* ️ Squared Partial Correlation (R²_partial) & Its Interpretation ️ R-Squared Change (ΔR²) for Unique Predictor Contribution 2. Hypothesis Testing for Individual Predictors ️ t-Tests for b-Coefficients (Significance T...

This document provides a detailed breakdown of regression with dummy variables, focusing on: 1. Understanding Dummy Variables in Regression ️ How to use dummy variables for categorical predictors ️ Comparing two or more group means using regression ️ Advantages of regression over ANOVA for categorical variables 2. Hypothesis Testing & Interpretation ️ Interpreting Regression Coefficients for Dummy Variables ️ Sum of Squares in Regression vs. ANOVA ️ Global F-Test for Grou...

Summary of the readings/literature required for Qualitative Innovation Analytics (GEO4-2260) Includes: * Oost, Heinze (2006), Circling around a Question: Defining your research problem (IVLOS). “Part I: Theory. Research Problems as a Concept”, pp. 5-10 * Oost, Heinze (2006), Circling around a Question: Defining your research problem (IVLOS). “Part II: Practice. Module 2: Relevance”, pp. 19-26 * Hancké, Bob (2009), 'Chapter 1: Research in the Social Sciences', Intelligent Resea...

All intended learning outcomes, sample questions, some important comparisons (dualism vs embeddedness, Hayek vs Polanyi, Neoclassical vs classical, etc)

This document is an exhaustive summary of all the material provided in the 2020/2021 Data Science Research Methods course. It includes in-depth descriptions of theory from the books Experimental Design (Berger et al., 2018) and Mostly Harmless Econometrics (Angrist et al., 2009) as well as the theory given in the lectures. Additionally, this 45-page document contains examples and quiz questions including worked out solutions to help you pass the exam!

In dit document zijn de leerdoelen voor het twee tentamen uitgewerkt aan de hand van de (engelse) slides. De tekst is voornamelijk in het Nederlands geschreven, maar aan het einde van de leerdoelen staat de tekst voornamelijk in het engels geschreven.

Datacamp introduction to Data Visualization with Matplotlib: - Customizing your plots - Small multiples - Plotting time-series data - Plotting time-series with different variables - Annotating time-series data - Quantitative comparisons: bar-charts - Quantitative comparisons: histograms - Statistical plotting - Quantitative comparisons: scatter plots - Preparing your figures to share with others - Sharing your visualizations with others - Automating figures from data - Where to go n...

This document contains concise and relevant information, all readings included, all principles of strategy included, references to the cases, comparisons across different weeks, ready for the exam.