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