.. _sphx_tag_topic-data-analysis:

My tags: topic: data analysis
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.. toctree::
    :maxdepth: 1
    :caption: With this tag

    ../learn/bayesian_data_analysis/001-the-three-steps-of-bayesian-data-analysis.rst
    ../learn/bayesian_data_analysis/002-general-notation-for-statistical-inference.rst
    ../learn/bayesian_data_analysis/003-bayesian-inference.rst
    ../learn/bayesian_data_analysis/004-discrete-bayesian-examples-genetics-and-spell-checking-with.rst
    ../learn/bayesian_data_analysis/005-probability-as-a-measure-of-uncertainty.rst
    ../learn/bayesian_data_analysis/006-example-probabilities-from-football-point-spreads.rst
    ../learn/bayesian_data_analysis/007-example-calibration-for-record-linkage.rst
    ../learn/bayesian_data_analysis/008-some-useful-results-from-probability-theory.rst
    ../learn/bayesian_data_analysis/009-computation-and-software.rst
    ../learn/bayesian_data_analysis/010-bayesian-inference-in-applied-statistics.rst
    ../learn/bayesian_data_analysis/011-estimating-a-probability-from-binomial-data.rst
    ../learn/bayesian_data_analysis/012-posterior-as-a-compromise-between-data-and-prior-information.rst
    ../learn/bayesian_data_analysis/013-summarizing-posterior-inference.rst
    ../learn/bayesian_data_analysis/014-informative-prior-distributions.rst
    ../learn/bayesian_data_analysis/015-normal-distribution-with-known-variance.rst
    ../learn/bayesian_data_analysis/016-other-standard-single-parameter-models.rst
    ../learn/bayesian_data_analysis/017-informative-prior-distribution-for-cancer-rates.rst
    ../learn/bayesian_data_analysis/018-noninformative-prior-distributions.rst
    ../learn/bayesian_data_analysis/019-weakly-informative-prior-distributions.rst
    ../learn/bayesian_data_analysis/020-averaging-over-nuisance-parameters.rst
    ../learn/bayesian_data_analysis/021-normal-data-with-a-noninformative-prior-distribution.rst
    ../learn/bayesian_data_analysis/022-normal-data-with-a-conjugate-prior-distribution.rst
    ../learn/bayesian_data_analysis/023-multinomial-model-for-categorical-data.rst
    ../learn/bayesian_data_analysis/024-multivariate-normal-model-with-known-variance.rst
    ../learn/bayesian_data_analysis/025-multivariate-normal-with-unknown-mean-and-variance.rst
    ../learn/bayesian_data_analysis/026-example-bayesian-analysis-of-a-bioassay-experiment-logistic-nonconjugate.rst
    ../learn/bayesian_data_analysis/027-summary-of-elementary-modeling-and-computation.rst
    ../learn/bayesian_data_analysis/028-normal-approximations-to-the-posterior-distribution.rst
    ../learn/bayesian_data_analysis/029-large-sample-theory.rst
    ../learn/bayesian_data_analysis/030-counterexamples-to-large-sample-asymptotic-bayesian-theorems.rst
    ../learn/bayesian_data_analysis/031-frequency-evaluations-of-bayesian-inferences.rst
    ../learn/bayesian_data_analysis/032-bayesian-interpretations-of-other-statistical-methods.rst
    ../learn/bayesian_data_analysis/033-constructing-a-parameterized-prior-distribution.rst
    ../learn/bayesian_data_analysis/034-exchangeability-and-hierarchical-models.rst
    ../learn/bayesian_data_analysis/035-bayesian-analysis-of-conjugate-hierarchical-models.rst
    ../learn/bayesian_data_analysis/036-normal-model-with-exchangeable-parameters.rst
    ../learn/bayesian_data_analysis/037-example-parallel-experiments-in-eight-schools.rst
    ../learn/bayesian_data_analysis/038-hierarchical-modeling-applied-to-a-meta-analysis.rst
    ../learn/bayesian_data_analysis/039-weakly-informative-priors-for-variance-parameters.rst
    ../learn/bayesian_data_analysis/040-the-place-of-model-checking-in-applied-bayesian-statistics.rst
    ../learn/bayesian_data_analysis/041-do-the-inferences-from-the-model-make-sense.rst
    ../learn/bayesian_data_analysis/042-posterior-predictive-checking.rst
    ../learn/bayesian_data_analysis/043-graphical-posterior-predictive-checks.rst
    ../learn/bayesian_data_analysis/044-model-checking-for-the-educational-testing-example.rst
    ../learn/bayesian_data_analysis/045-measures-of-predictive-accuracy.rst
    ../learn/bayesian_data_analysis/046-model-comparison-based-on-predictive-performance.rst
    ../learn/bayesian_data_analysis/047-model-comparison-using-bayes-factors.rst
    ../learn/bayesian_data_analysis/048-continuous-model-expansion.rst
    ../learn/bayesian_data_analysis/049-implicit-assumptions-and-model-expansion-an-example.rst
    ../learn/bayesian_data_analysis/050-bayesian-inference-requires-a-model-for-data-collection.rst
    ../learn/bayesian_data_analysis/051-data-collection-models-and-ignorability.rst
    ../learn/bayesian_data_analysis/052-sample-surveys.rst
    ../learn/bayesian_data_analysis/053-designed-experiments.rst
    ../learn/bayesian_data_analysis/054-sensitivity-and-the-role-of-randomization.rst
    ../learn/bayesian_data_analysis/055-observational-studies.rst
    ../learn/bayesian_data_analysis/056-censoring-and-truncation.rst
    ../learn/bayesian_data_analysis/057-bayesian-decision-theory-in-different-contexts.rst
    ../learn/bayesian_data_analysis/058-using-regression-predictions-survey-incentives.rst
    ../learn/bayesian_data_analysis/059-multistage-decision-making-medical-screening.rst
    ../learn/bayesian_data_analysis/060-hierarchical-decision-analysis-for-home-radon.rst
    ../learn/bayesian_data_analysis/061-personal-vs-institutional-decision-analysis.rst
    ../learn/bayesian_data_analysis/062-numerical-integration.rst
    ../learn/bayesian_data_analysis/063-distributional-approximations.rst
    ../learn/bayesian_data_analysis/064-direct-simulation-and-rejection-sampling.rst
    ../learn/bayesian_data_analysis/065-importance-sampling.rst
    ../learn/bayesian_data_analysis/066-how-many-simulation-draws-are-needed.rst
    ../learn/bayesian_data_analysis/067-computing-environments.rst
    ../learn/bayesian_data_analysis/068-debugging-bayesian-computing.rst
    ../learn/bayesian_data_analysis/069-gibbs-sampler.rst
    ../learn/bayesian_data_analysis/070-metropolis-and-metropolis-hastings-algorithms.rst
    ../learn/bayesian_data_analysis/071-using-gibbs-and-metropolis-as-building-blocks.rst
    ../learn/bayesian_data_analysis/072-inference-and-assessing-convergence.rst
    ../learn/bayesian_data_analysis/073-effective-number-of-simulation-draws.rst
    ../learn/bayesian_data_analysis/074-example-hierarchical-normal-model.rst
    ../learn/bayesian_data_analysis/075-efficient-gibbs-samplers.rst
    ../learn/bayesian_data_analysis/076-efficient-metropolis-jumping-rules.rst
    ../learn/bayesian_data_analysis/077-further-extensions-to-gibbs-and-metropolis.rst
    ../learn/bayesian_data_analysis/078-hamiltonian-monte-carlo.rst
    ../learn/bayesian_data_analysis/079-hamiltonian-monte-carlo-for-a-hierarchical-model.rst
    ../learn/bayesian_data_analysis/080-stan-developing-a-computing-environment.rst
    ../learn/bayesian_data_analysis/081-finding-posterior-modes.rst
    ../learn/bayesian_data_analysis/082-boundary-avoiding-priors-for-modal-summaries.rst
    ../learn/bayesian_data_analysis/083-normal-and-related-mixture-approximations.rst
    ../learn/bayesian_data_analysis/084-finding-marginal-posterior-modes-using-em.rst
    ../learn/bayesian_data_analysis/085-conditional-and-marginal-posterior-approximations.rst
    ../learn/bayesian_data_analysis/086-example-hierarchical-normal-model-continued.rst
    ../learn/bayesian_data_analysis/087-variational-inference.rst
    ../learn/bayesian_data_analysis/088-expectation-propagation.rst
    ../learn/bayesian_data_analysis/089-other-approximations.rst
    ../learn/bayesian_data_analysis/090-unknown-normalizing-factors.rst
    ../learn/bayesian_data_analysis/091-conditional-modeling.rst
    ../learn/bayesian_data_analysis/092-bayesian-analysis-of-classical-regression.rst
    ../learn/bayesian_data_analysis/093-regression-for-causal-inference-incumbency-and-voting.rst
    ../learn/bayesian_data_analysis/094-goals-of-regression-analysis.rst
    ../learn/bayesian_data_analysis/095-assembling-the-matrix-of-explanatory-variables.rst
    ../learn/bayesian_data_analysis/096-regularization-and-dimension-reduction.rst
    ../learn/bayesian_data_analysis/097-unequal-variances-and-correlations.rst
    ../learn/bayesian_data_analysis/098-including-numerical-prior-information.rst
    ../learn/bayesian_data_analysis/099-regression-coefficients-exchangeable-in-batches.rst
    ../learn/bayesian_data_analysis/100-example-forecasting-u-s-presidential-elections.rst
    ../learn/bayesian_data_analysis/101-interpreting-a-normal-prior-distribution-as-extra-data.rst
    ../learn/bayesian_data_analysis/102-varying-intercepts-and-slopes.rst
    ../learn/bayesian_data_analysis/103-computation-batching-and-transformation.rst
    ../learn/bayesian_data_analysis/104-analysis-of-variance-and-the-batching-of-coefficients.rst
    ../learn/bayesian_data_analysis/105-hierarchical-models-for-batches-of-variance-components.rst
    ../learn/bayesian_data_analysis/106-standard-generalized-linear-model-likelihoods.rst
    ../learn/bayesian_data_analysis/107-working-with-generalized-linear-models.rst
    ../learn/bayesian_data_analysis/108-weakly-informative-priors-for-logistic-regression.rst
    ../learn/bayesian_data_analysis/109-overdispersed-poisson-regression-for-police-stops.rst
    ../learn/bayesian_data_analysis/110-state-level-opinons-from-national-polls.rst
    ../learn/bayesian_data_analysis/111-models-for-multivariate-and-multinomial-responses.rst
    ../learn/bayesian_data_analysis/112-loglinear-models-for-multivariate-discrete-data.rst
    ../learn/bayesian_data_analysis/113-aspects-of-robustness.rst
    ../learn/bayesian_data_analysis/114-overdispersed-versions-of-standard-models.rst
    ../learn/bayesian_data_analysis/115-posterior-inference-and-computation.rst
    ../learn/bayesian_data_analysis/116-robust-inference-for-the-eight-schools.rst
    ../learn/bayesian_data_analysis/117-robust-regression-using-t-distributed-errors.rst
    ../learn/bayesian_data_analysis/118-notation.rst
    ../learn/bayesian_data_analysis/119-multiple-imputation.rst
    ../learn/bayesian_data_analysis/120-missing-data-in-the-multivariate-normal-and-t-models.rst
    ../learn/bayesian_data_analysis/121-example-multiple-imputation-for-a-series-of-polls.rst
    ../learn/bayesian_data_analysis/122-missing-values-with-counted-data.rst
    ../learn/bayesian_data_analysis/123-example-an-opinion-poll-in-slovenia.rst
    ../learn/bayesian_data_analysis/124-example-serial-dilution-assay.rst
    ../learn/bayesian_data_analysis/125-example-population-toxicokinetics.rst
    ../learn/bayesian_data_analysis/126-splines-and-weighted-sums-of-basis-functions.rst
    ../learn/bayesian_data_analysis/127-basis-selection-and-shrinkage-of-coefficients.rst
    ../learn/bayesian_data_analysis/128-non-normal-models-and-regression-surfaces.rst
    ../learn/bayesian_data_analysis/129-gaussian-process-regression.rst
    ../learn/bayesian_data_analysis/130-example-birthdays-and-birthdates.rst
    ../learn/bayesian_data_analysis/131-latent-gaussian-process-models.rst
    ../learn/bayesian_data_analysis/132-functional-data-analysis.rst
    ../learn/bayesian_data_analysis/133-density-estimation-and-regression.rst
    ../learn/bayesian_data_analysis/134-setting-up-and-interpreting-mixture-models.rst
    ../learn/bayesian_data_analysis/135-example-reaction-times-and-schizophrenia.rst
    ../learn/bayesian_data_analysis/136-label-switching-and-posterior-computation.rst
    ../learn/bayesian_data_analysis/137-unspecified-number-of-mixture-components.rst
    ../learn/bayesian_data_analysis/138-mixture-models-for-classification-and-regression.rst
    ../learn/bayesian_data_analysis/139-bayesian-histograms.rst
    ../learn/bayesian_data_analysis/140-dirichlet-process-prior-distributions.rst
    ../learn/bayesian_data_analysis/141-dirichlet-process-mixtures.rst
    ../learn/bayesian_data_analysis/142-beyond-density-estimation.rst
    ../learn/bayesian_data_analysis/143-hierarchical-dependence.rst
    ../learn/bayesian_data_analysis/144-density-regression.rst
    ../learn/bayesian_data_analysis/index.rst
    ../learn/data_preparation_and_analysis/01-why-do-we-analyze-data.rst
    ../learn/data_preparation_and_analysis/02-the-process-of-data-analysis.rst
    ../learn/data_preparation_and_analysis/03-crisp-dm-for-data-science.rst
    ../learn/data_preparation_and_analysis/04-big-data-definition-characteristics-evolution-and-business-impact.rst
    ../learn/data_preparation_and_analysis/05-the-first-step-in-knowing-your-data.rst
    ../learn/data_preparation_and_analysis/06-ieee-754-floating-point-standard.rst
    ../learn/data_preparation_and_analysis/07-discovering-associations-through-data-from-everyday-patterns-to-chicago-taxi-trips-september-2022.rst
    ../learn/data_preparation_and_analysis/08-taxi-trips-2022-dataset-from-the-city-of-chicago-open-data-portal.rst
    ../learn/data_preparation_and_analysis/09-objective-selection-of-the-bin-width-for-a-time-histogram.rst
    ../learn/data_preparation_and_analysis/10-measuring-associations-in-data.rst
    ../learn/data_preparation_and_analysis/11-measuring-associations-between-two-continuous-variables.rst
    ../learn/data_preparation_and_analysis/12-correlation-coefficients-in-python-pearson-spearman-kendall.rst
    ../learn/data_preparation_and_analysis/13-karl-pearson.rst
    ../learn/data_preparation_and_analysis/14-harald-cramer.rst
    ../learn/data_preparation_and_analysis/15-what-are-statistical-tests.rst
    ../learn/data_preparation_and_analysis/16-eta-squared-2-effect-size-in-anova.rst
    ../learn/data_preparation_and_analysis/17-understanding-market-baskets-and-ideal-customers.rst
    ../learn/data_preparation_and_analysis/18-what-can-association-rules-tell-us.rst
    ../learn/data_preparation_and_analysis/19-how-association-rules-are-discovered-concepts-scale-measures-and-the-apriori-approach.rst
    ../learn/data_preparation_and_analysis/20-apriori-frequent-itemsets-via-the-apriori-algorithm.rst
    ../learn/data_preparation_and_analysis/21-association-rules-generating-association-rules-from-frequent-itemsets-mlxtend.rst
    ../learn/data_preparation_and_analysis/22-cross-selling.rst
    ../learn/data_preparation_and_analysis/23-stratified-random-sampling.rst
    ../learn/data_preparation_and_analysis/24-linear-congruential-random-number-generator-lcg.rst
    ../learn/data_preparation_and_analysis/25-partitioning-observations-to-train-objective-models.rst
    ../learn/data_preparation_and_analysis/26-putting-similar-observations-into-clusters.rst
    ../learn/data_preparation_and_analysis/27-clustering.rst
    ../learn/data_preparation_and_analysis/28-recency-frequency-and-monetary-value-rfm.rst
    ../learn/data_preparation_and_analysis/29-rfm-analysis.rst
    ../learn/data_preparation_and_analysis/30-creating-segments-of-observations-for-business-reasons-rfm.rst
    ../learn/data_preparation_and_analysis/31-least-squares-regression.rst
    ../learn/data_preparation_and_analysis/32-multiple-linear-regression.rst
    ../learn/data_preparation_and_analysis/33-feature-importance-in-linear-regression.rst
    ../learn/data_preparation_and_analysis/34-forward-selection-definition-and-core-idea.rst
    ../learn/data_preparation_and_analysis/35-forward-selection-and-model-interpretation-in-linear-regression.rst
    ../learn/data_preparation_and_analysis/36-understanding-forward-and-backward-stepwise-regression.rst
    ../learn/data_preparation_and_analysis/37-how-shapley-values-work.rst
    ../learn/data_preparation_and_analysis/38-logistic-regression-modeling-binary-outcomes-via-odds-and-log-odds.rst
    ../learn/data_preparation_and_analysis/39-maximum-likelihood-mle-fitting-a-distribution-to-observed-data.rst
    ../learn/data_preparation_and_analysis/40-assessing-model-fit-in-logistic-regression.rst
    ../learn/data_preparation_and_analysis/41-complete-and-quasi-complete-separation-in-logistic-regression.rst
    ../learn/data_preparation_and_analysis/42-forward-selection-with-nested-models-and-deviance-tests.rst
    ../learn/data_preparation_and_analysis/43-interpreting-and-assessing-a-forward-selection-logistic-regression-model-for-college-student-retention.rst
    ../learn/data_preparation_and_analysis/44-motivation-of-decision-trees-an-incremental-model-of-decision-making.rst
    ../learn/data_preparation_and_analysis/45-the-cart-algorithm.rst
    ../learn/data_preparation_and_analysis/46-decision-trees-as-piecewise-models-and-their-predictive-structure.rst
    ../learn/data_preparation_and_analysis/47-how-cart-decision-trees-model-interactions.rst
    ../learn/data_preparation_and_analysis/48-cluster-profiling-using-decision-trees.rst
    ../learn/data_preparation_and_analysis/49-using-decision-trees-to-explain-clustering-results.rst
    ../learn/data_preparation_and_analysis/50-assessing-the-quality-of-prediction-models.rst
    ../learn/data_preparation_and_analysis/51-binary-classification-models-conceptual-framework-and-evaluation-metrics.rst
    ../learn/data_preparation_and_analysis/52-nominal-classification-models-model-state-and-evaluation-metrics.rst
    ../learn/data_preparation_and_analysis/53-binary-classification-model-evaluation-and-threshold-optimization.rst
    ../learn/data_preparation_and_analysis/54-identifying-outliers-using-residuals-and-studentized-residuals.rst
    ../learn/data_preparation_and_analysis/55-auc-roc-curve-evaluating-classification-model-performance.rst
    ../learn/data_preparation_and_analysis/56-lift-analysis-for-direct-mail-campaigns-concept-process-and-business-value.rst
    ../learn/data_preparation_and_analysis/index.rst