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Statements

Subject Item: dbr:Multilevel_Regression_with_Poststratification
dbo:wikiPageWikiLink: dbr:Multilevel_regression_with_poststratification
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Subject Item: dbr:Opinion_polling_for_the_2019_European_Parliament_election_in_the_United_Kingdom
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Subject Item: dbr:Fay-Herriot_model
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Subject Item: dbr:2021_Hartlepool_by-election
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Subject Item: dbr:Leeds_North_East_(UK_Parliament_constituency)
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Subject Item: dbr:Opinion_polling_for_the_2019_United_Kingdom_general_election
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Subject Item: dbr:Opinion_polling_for_the_next_United_Kingdom_general_election
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Subject Item: dbr:YouGov
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Subject Item: dbr:MRP
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Subject Item: dbr:Multilevel_regression_with_poststratification
rdfs:label: Multilevel regression with poststratification
rdfs:comment: Multilevel regression with poststratification (MRP) (sometimes called "Mister P") is a statistical technique used for correcting model estimates for known differences between a sample population (the population of the data you have), and a target population (a population you would like to estimate for). For example, Wang et al. used survey data from Xbox gamers to predict U.S. presidential election results. The Xbox gamers were 65% 18- to 29-year-olds and 93% male, while the electorate as a whole was 19% 18- to 29-year-olds and 47% male.
dcterms:subject: dbc:Regression_models dbc:Analysis_of_variance
dbo:wikiPageID: 62207342
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dbp:wikiPageUsesTemplate: dbt:Regression_bar dbt:Reflist dbt:Short_description
dbo:abstract: Multilevel regression with poststratification (MRP) (sometimes called "Mister P") is a statistical technique used for correcting model estimates for known differences between a sample population (the population of the data you have), and a target population (a population you would like to estimate for). For example, Wang et al. used survey data from Xbox gamers to predict U.S. presidential election results. The Xbox gamers were 65% 18- to 29-year-olds and 93% male, while the electorate as a whole was 19% 18- to 29-year-olds and 47% male. The poststratification refers to the process of adjusting the estimates, essentially a weighted average of estimates from all possible combinations of attributes (in this example age and sex, though there were more). Each combination is sometimes called a "cell." The multilevel regression is used to smooth noisy estimates in the cells with too little data by using overall or nearby averages. One application is estimating preferences in sub-regions (e.g., states, individual constituencies) based on individual-level survey data gathered at other levels of aggregation (e.g., national surveys).
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Subject Item: dbr:Multilevel_Regression_and_Post-stratification
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Subject Item: dbr:Multilevel_Regression_and_Poststratification
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Subject Item: dbr:Multilevel_regression_and_poststratification
dbo:wikiPageWikiLink: dbr:Multilevel_regression_with_poststratification
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Subject Item: wikipedia-en:Multilevel_regression_with_poststratification
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