{"id":23255415,"url":"https://github.com/dnce17/nhanes_inferential_2023","last_synced_at":"2025-04-06T03:42:46.381Z","repository":{"id":261694318,"uuid":"883864578","full_name":"dnce17/nhanes_inferential_2023","owner":"dnce17","description":"Performing inferential statistics on NHANES 2021-2023 data using pandas, matplotlib, numpy, seaborn, scipy, and statsmodels","archived":false,"fork":false,"pushed_at":"2024-12-09T17:03:26.000Z","size":99,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-12T09:59:29.571Z","etag":null,"topics":["inferential-statistics"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dnce17.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-05T17:53:50.000Z","updated_at":"2024-12-09T17:03:29.000Z","dependencies_parsed_at":"2024-11-07T23:28:51.910Z","dependency_job_id":"77701d6e-451e-4bd2-bf19-7f9764417a3f","html_url":"https://github.com/dnce17/nhanes_inferential_2023","commit_stats":null,"previous_names":["dnce17/nhanes_inferential_2023"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnce17%2Fnhanes_inferential_2023","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnce17%2Fnhanes_inferential_2023/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnce17%2Fnhanes_inferential_2023/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnce17%2Fnhanes_inferential_2023/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dnce17","download_url":"https://codeload.github.com/dnce17/nhanes_inferential_2023/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247430837,"owners_count":20937873,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["inferential-statistics"],"created_at":"2024-12-19T11:27:54.623Z","updated_at":"2025-04-06T03:42:46.359Z","avatar_url":"https://github.com/dnce17.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Performing Inferential Statistics on NHANES 2021-2023 Data \n\n## NHANES Data\n* [NHANES 2021-2023](https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?Cycle=2021-2023)\n\n## Data Used + Links\n* Demographics\n   * Location: Demographics Data (of NHANES data link)\n* Physical Activity\n   * Location: Questionnaire Data\n* Blood Pressure\n   * Location: Examination Data\n* Weight\n   * Location: Questionnaire Data\n* Vitamin D\n   * Location: Laboratory Data\n\n## Project Description\nThis project focuses on performing basic inferential statistics on NHANES data using Python. Relationships and differences in health metrics and demographics are explored. \n\nHealth metrics used in this project:\n* sedentary time\n* systolic blood pressure (BP)\n* vitamin D levels\n* weight \n\nDemographics attributes used:\n* marital status\n* education level\n* age\n\n## Questions Explored, Analysis Test Done, and Results\n1. Is there an association between marital status (married or not married) and education level (bachelor’s degree or higher vs. less than a bachelor’s degree)?\n    * **Analysis Test performed**: Chi-square\n        * Marital status - categorical\n        * Education level - categorical\n    * **Result**: There is an association b/w marital status and education level \n2. Is there a difference in the mean sedentary behavior time between those who are married and those who are not married?\n    * **Analysis Test**: T-test\n        * Sedentary behavior time - continuous (quantitative)\n            * dependent variable (DV)\n        * Marital status - categorical\n            * indepedent variable (IV)\n    * **Result**: Significant difference between the groups\n3. How do age and marital status affect systolic BP?\n    * **Analysis Test**: Regression\n        * Age - continuous (quantitative)\n            * IV\n        * Marital status - categorical\n            * IV\n        * Systolic BP - continuous (quantitative)\n            * DV\n    * **Result**: Both are statistically significant and thus, affects systolic BP. However, age has a stronger effect on systolic BP than marital status.\n4. Is there a correlation between self-reported weight and minutes of sedentary behavior?\n    * **Analysis Test**: Correlation\n        * Weight - continuous (quantitative)\n        * Sedentary behavior time - continuous (quantitative)\n    * **Result**: Weak correlation between the two variables\n\n### Creative Analysis\n**Instruction**: Develop your own unique question using at least one of the variables listed above. Ensure that your question can be answered using one of the following tests: chi-square, t-test, ANOVA, or correlation. Clearly state your question, explain why you chose the test, and document your findings. \n\n5. Is there a difference in the sedentary behavior time of those who have adequate vitamin D levels vs those who do not?\n    * **Analysis Test**: T-test\n        * Mean sedentary behavior time - continuous (quantitative)\n            * DV\n        * Vitamin D - categorical\n            * IV\n    * T-test is used for this question b/c there is a categorical variable with exactly 2 levels (those with and without adequate vitamin D levels) and a quantitative variable. T-test works on these variables types. \n    * **Result**: No significant difference between sedentary behavior time and those with and without adequate vitamin D levels\n\n## Note regarding analysis\n* Much of the data cleaning involves converting SEQN (participant unique identifer) to a str/obj, which is needed to merge various data together for analysis.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdnce17%2Fnhanes_inferential_2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdnce17%2Fnhanes_inferential_2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdnce17%2Fnhanes_inferential_2023/lists"}