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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.
- Dataviz and probability;/statistics/visualizations_example;1
- Periodic splines;/statistics/periodic_splines;2
- Directional statistics;/statistics/directional;3
- Prior sensitivity analysis;/statistics/sensitivity;4
- Horseshoe priors;/statistics/horseshoe;5
- MRP;/statistics/mrp;6
- Application of the Lotka-Volterra model;/statistics/lotka_volterra;7
- Differential equations;/statistics/ode;8
- Dirichlet Process Mixture Models;/statistics/dp;9
- Bayesian Additive Regression Trees;/statistics/bart;10
- Splines;/statistics/spline;11
- Gaussian processes regression;/statistics/gp_example;12
- Gaussian processes;/statistics/gp;13
- Nonparametric models;/statistics/nonparametric_intro;14
- Stochastic volatility models;/statistics/stochastic_volatility;15
- Structural time series;/statistics/structural_time_series;16
- Time series;/statistics/time_series;17
- Quantile regression;/statistics/extreme_quantile;18
- Introduction to Extreme Values theory;/statistics/extreme_intro;19
- Frailty models and cure rate models;/statistics/survival_example_frailty;20
- Accelerated Failure Time models;/statistics/survival_example_aft;21
- Application of survival analysis with discrete times;/statistics/survival_example_2;22
- Application of survival analysis 1;/statistics/survival_example;23
- Introduction to survival analysis;/statistics/survival_analysis;24
- Regression discontinuity design;/statistics/rdd;25
- Difference in difference;/statistics/difference_in_differences;26
- Instrumental variable regression;/statistics/instrumental_variable;27
- Randomized controlled trials;/statistics/randomized;28
- Validity;/statistics/validity;29
- Causal inference and Bayesian networks;/statistics/causal_intro_2;30
- Causal inference;/statistics/causal_intro;31
- Nested factor;/statistics/nested_factors;32
- Repeated measures;/statistics/repeated_measures;33
- Split plot design;/statistics/split_plot;34
- Crossover design;/statistics/crossover;35
- Latin square design;/statistics/latin_square;36
- Full factorial design;/statistics/full_factorial;37
- Completely randomized design;/statistics/crd;38
- Design of experiments;/statistics/doe;39
- Stratification;/statistics/stratification;40
- Random sampling;/statistics/random_sampling;41
- Data collection;/statistics/data_collection;42
- Things that could go wrong;/statistics/problem_solving_issues;43
- The problem solving workflow;/statistics/problem_solving;44
- Mixed effects models with more than two levels;/statistics/three_levels;45
- Leveraging mixed-effect models;/statistics/bambi_multilevel;46
- Random models and mixed models;/statistics/random_models;47
- Hierarchical models and meta-analysis;/statistics/hierarchical_metaanalysis;48
- Hierarchical models;/statistics/hierarchical_models;49
- Poisson regression;/statistics/poisson_regression;50
- Logistic regression;/statistics/logistic_regression;51
- Robust linear regression;/statistics/robust_regression;52
- Multi-linear regression;/statistics/multivariate_regression;53
- Linear regression with binary input;/statistics/regression_binary_input;54
- Introduction to the linear regression;/statistics/regression;55
- Model comparison, cont.;/statistics/model_averaging_cont;56
- Model comparison;/statistics/model_averaging;57
- Re-parametrizing your model;/statistics/reparametrization;58
- Predictive checks;/statistics/predictive_checks;59
- Trace inspection;/statistics/trace_inspection;60
- Introduction to the Bayesian workflow;/statistics/bayesian_workflow;61
- Mixture models;/statistics/mixture;62
- Multidimensional distributions;/statistics/categories;63
- The Gaussian model;/statistics/reals;64
- Bonus: counting animals in a park;/statistics/hypergeom;65
- The Negative Binomial model;/statistics/negbin;66
- The Poisson model;/statistics/poisson;67
- The Beta-Binomial model;/statistics/betabin;68
- Section introduction;/statistics/simple_models_intro;69
- Some notation about probability;/statistics/probability_reminder;70
- How does MCMC works;/statistics/mcmc_intro;71
- Introduction to Bayesian inference;/statistics/bayes_intro;72
- An overview to statistics;/statistics/preface;73
- The Gestalt principles;/dataviz/gestalt;74
- Design tricks;/dataviz/design-introduction;75
- How to choose a color map;/dataviz/palettes-introduction;76
- Introduction to color perception;/dataviz/color-introduction;77
- Drawing is redrawing;/dataviz/gender-economist;78
- Visual queries;/dataviz/visual-queries;79
- Channel effectiveness;/dataviz/effectiveness;80
- Evolutions of the line chart;/dataviz/linechart-evolution;81
- Beyond the 1D scatterplot;/dataviz/scatterplot-evolution;82
- Perception;/dataviz/perception;83
- Fundamental charts;/dataviz/fundamental-charts;84
- Marks and channels;/dataviz/marks-channels;85
- Data abstraction;/dataviz/data-types;86
- Data visualization;/dataviz/dataviz;87
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