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