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