Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386561536> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W4386561536 endingPage "665" @default.
- W4386561536 startingPage "659" @default.
- W4386561536 abstract "Abstract Background: In this article, we attempt to demonstrate the superiority of the Bayesian approach over the frequentist approaches of the multiple linear regression model in identifying the influencing factors for the response variable. Methods and Material: A survey was conducted among the 310 respondents from the Kathirkamam area in Puducherry. We have considered the response variable, body mass index (BMI), and the predictors such as age, weight, gender, nature of the job, and marital status of individuals were collected with the personal interview method. Jeffreys’s Amazing Statistics Program (JASP) software was used to analyze the dataset. In the conventional multiple linear regression model, the single value of regression coefficients is determined, while in the Bayesian linear regression model, the regression coefficient of each predictor follows a specific posterior distribution. Furthermore, it would be most useful to identify the best models from the list of possible models with posterior probability values. An inclusion probability for all the predictors will give a superior idea of whether the predictors are included in the model with probability. Results and Conclusions: The Bayesian framework offers a wide range of results for the regression coefficients instead of the single value of regression coefficients in the frequentist test. Such advantages of the Bayesian approach will catapult the quality of investigation outputs by giving more reliability to solutions of scientific problems." @default.
- W4386561536 created "2023-09-09" @default.
- W4386561536 creator A5012247258 @default.
- W4386561536 creator A5019793888 @default.
- W4386561536 creator A5024170009 @default.
- W4386561536 date "2023-01-01" @default.
- W4386561536 modified "2023-10-16" @default.
- W4386561536 title "Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study" @default.
- W4386561536 cites W1989570555 @default.
- W4386561536 cites W2020389170 @default.
- W4386561536 cites W2081861598 @default.
- W4386561536 cites W2337874535 @default.
- W4386561536 cites W2789555072 @default.
- W4386561536 cites W2794003188 @default.
- W4386561536 cites W2945386046 @default.
- W4386561536 cites W3020777486 @default.
- W4386561536 cites W3029917446 @default.
- W4386561536 cites W3089151861 @default.
- W4386561536 cites W3118548831 @default.
- W4386561536 doi "https://doi.org/10.4103/ijcm.ijcm_119_22" @default.
- W4386561536 hasPublicationYear "2023" @default.
- W4386561536 type Work @default.
- W4386561536 citedByCount "0" @default.
- W4386561536 crossrefType "journal-article" @default.
- W4386561536 hasAuthorship W4386561536A5012247258 @default.
- W4386561536 hasAuthorship W4386561536A5019793888 @default.
- W4386561536 hasAuthorship W4386561536A5024170009 @default.
- W4386561536 hasBestOaLocation W43865615361 @default.
- W4386561536 hasConcept C105795698 @default.
- W4386561536 hasConcept C107673813 @default.
- W4386561536 hasConcept C149782125 @default.
- W4386561536 hasConcept C152877465 @default.
- W4386561536 hasConcept C160234255 @default.
- W4386561536 hasConcept C162376815 @default.
- W4386561536 hasConcept C32224588 @default.
- W4386561536 hasConcept C33923547 @default.
- W4386561536 hasConcept C37903108 @default.
- W4386561536 hasConcept C41587187 @default.
- W4386561536 hasConcept C48921125 @default.
- W4386561536 hasConcept C64946054 @default.
- W4386561536 hasConcept C83546350 @default.
- W4386561536 hasConceptScore W4386561536C105795698 @default.
- W4386561536 hasConceptScore W4386561536C107673813 @default.
- W4386561536 hasConceptScore W4386561536C149782125 @default.
- W4386561536 hasConceptScore W4386561536C152877465 @default.
- W4386561536 hasConceptScore W4386561536C160234255 @default.
- W4386561536 hasConceptScore W4386561536C162376815 @default.
- W4386561536 hasConceptScore W4386561536C32224588 @default.
- W4386561536 hasConceptScore W4386561536C33923547 @default.
- W4386561536 hasConceptScore W4386561536C37903108 @default.
- W4386561536 hasConceptScore W4386561536C41587187 @default.
- W4386561536 hasConceptScore W4386561536C48921125 @default.
- W4386561536 hasConceptScore W4386561536C64946054 @default.
- W4386561536 hasConceptScore W4386561536C83546350 @default.
- W4386561536 hasIssue "5" @default.
- W4386561536 hasLocation W43865615361 @default.
- W4386561536 hasOpenAccess W4386561536 @default.
- W4386561536 hasPrimaryLocation W43865615361 @default.
- W4386561536 hasRelatedWork W1519288722 @default.
- W4386561536 hasRelatedWork W2290902902 @default.
- W4386561536 hasRelatedWork W2358754556 @default.
- W4386561536 hasRelatedWork W2363843476 @default.
- W4386561536 hasRelatedWork W2364467608 @default.
- W4386561536 hasRelatedWork W2375721435 @default.
- W4386561536 hasRelatedWork W2889116641 @default.
- W4386561536 hasRelatedWork W3175130226 @default.
- W4386561536 hasRelatedWork W4242964699 @default.
- W4386561536 hasRelatedWork W65314033 @default.
- W4386561536 hasVolume "48" @default.
- W4386561536 isParatext "false" @default.
- W4386561536 isRetracted "false" @default.
- W4386561536 workType "article" @default.