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A personal blog about data visualization and data analysis.
Welcome to my blog!
Here I will share some ideas about dataviz and data science.
- Regression discontinuity design;/statistics/rdd;1
- Difference in difference;/statistics/difference_in_differences;2
- Instrumental variable regression;/statistics/instrumental_variable;3
- Randomized controlled trials;/statistics/randomized;4
- Validity;/statistics/validity;5
- Causal inference and Bayesian networks;/statistics/causal_intro_2;6
- Causal inference;/statistics/causal_intro;7
- Nested factor;/statistics/nested_factors;8
- Repeated measures;/statistics/repeated_measures;9
- Split plot design;/statistics/split_plot;10
- Crossover design;/statistics/crossover;11
- Latin square design;/statistics/latin_square;12
- Full factorial design;/statistics/full_factorial;13
- Completely randomized design;/statistics/crd;14
- Design of experiments;/statistics/doe;15
- Stratification;/statistics/stratification;16
- Random sampling;/statistics/random_sampling;17
- Data collection;/statistics/data_collection;18
- Things that could go wrong;/statistics/problem_solving_issues;19
- The problem solving workflow;/statistics/problem_solving;20
- Mixed effects models with more than two levels;/statistics/three_levels;21
- Leveraging mixed-effect models;/statistics/bambi_multilevel;22
- Random models and mixed models;/statistics/random_models;23
- Hierarchical models and meta-analysis;/statistics/hierarchical_metaanalysis;24
- Hierarchical models;/statistics/hierarchical_models;25
- Poisson regression;/statistics/poisson_regression;26
- Logistic regression;/statistics/logistic_regression;27
- Robust linear regression;/statistics/robust_regression;28
- Multi-linear regression;/statistics/multivariate_regression;29
- Linear regression with binary input;/statistics/regression_binary_input;30
- Introduction to the linear regression;/statistics/regression;31
- Model comparison, cont.;/statistics/model_averaging_cont;32
- Model comparison;/statistics/model_averaging;33
- Re-parametrizing your model;/statistics/reparametrization;34
- Predictive checks;/statistics/predictive_checks;35
- Trace inspection;/statistics/trace_inspection;36
- Introduction to the Bayesian workflow;/statistics/bayesian_workflow;37
- Mixture models;/statistics/mixture;38
- Multidimensional distributions;/statistics/categories;39
- The Gaussian model;/statistics/reals;40
- Bonus: counting animals in a park;/statistics/hypergeom;41
- The Negative Binomial model;/statistics/negbin;42
- The Poisson model;/statistics/poisson;43
- The Beta-Binomial model;/statistics/betabin;44
- Section introduction;/statistics/simple_models_intro;45
- Some notation about probability;/statistics/probability_reminder;46
- How does MCMC works;/statistics/mcmc_intro;47
- Introduction to Bayesian inference;/statistics/bayes_intro;48
- An overview to statistics;/statistics/preface;49
- The Gestalt principles;/dataviz/gestalt;50
- Design tricks;/dataviz/design-introduction;51
- How to choose a color map;/dataviz/palettes-introduction;52
- Introduction to color perception;/dataviz/color-introduction;53
- Drawing is redrawing;/dataviz/gender-economist;54
- Visual queries;/dataviz/visual-queries;55
- Channel effectiveness;/dataviz/effectiveness;56
- Evolutions of the line chart;/dataviz/linechart-evolution;57
- Beyond the 1D scatterplot;/dataviz/scatterplot-evolution;58
- Perception;/dataviz/perception;59
- Fundamental charts;/dataviz/fundamental-charts;60
- Marks and channels;/dataviz/marks-channels;61
- Data abstraction;/dataviz/data-types;62
- Data visualization;/dataviz/dataviz;63
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