{"id":17894030,"url":"https://github.com/grantgasser/complete-multiple-regression","last_synced_at":"2025-04-03T04:18:22.658Z","repository":{"id":129537150,"uuid":"151129112","full_name":"grantgasser/Complete-Multiple-Regression","owner":"grantgasser","description":"R script that performs complete multiple regression on two data sets","archived":false,"fork":false,"pushed_at":"2018-10-01T17:38:38.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-08T18:14:38.408Z","etag":null,"topics":["least-squares","linear-models","multiple-regression","r"],"latest_commit_sha":null,"homepage":null,"language":"R","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/grantgasser.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":"2018-10-01T17:30:08.000Z","updated_at":"2018-10-13T04:20:26.000Z","dependencies_parsed_at":"2023-04-22T07:47:57.251Z","dependency_job_id":null,"html_url":"https://github.com/grantgasser/Complete-Multiple-Regression","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FComplete-Multiple-Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FComplete-Multiple-Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FComplete-Multiple-Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FComplete-Multiple-Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/grantgasser","download_url":"https://codeload.github.com/grantgasser/Complete-Multiple-Regression/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246933385,"owners_count":20857055,"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":["least-squares","linear-models","multiple-regression","r"],"created_at":"2024-10-28T14:59:06.045Z","updated_at":"2025-04-03T04:18:22.637Z","avatar_url":"https://github.com/grantgasser.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Complete Multiple Regression\n\n## Description\nThis R script performs a complete multiple regression on two data sets, using the `lm` function and different plots and tests to analyze the following assumptions of the model:\n* The residuals are normally distributed \n* The residuals are not correlated\n* The residuals have constant variance\n\nThe script also constructs a confidence interval and prediction interval for each data set.\n\n## Data\n### Grocery Store\n#### A dataset from the textbook Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, \u0026 Li\nThis [dataset](http://users.stat.ufl.edu/~rrandles/sta4210/Rclassnotes/data/textdatasets/KutnerData/Chapter%20%206%20Data%20Sets/CH06PR09.txt) is based on the following: A large, national grocery retailer tracks productivity and costs of its facilities\nclosely. Data below were obtained from a single distribution center for a one-year period. Each\ndata point for each variable represents one week of activity. The variables included are the\nnumber of cases shipped (X1) the indirect costs of the total labor hours as a percentage (X2),\na qualitative predictor called holiday that is coded 1 if the week has a holiday and 0 otherwise\n(X3), and the total labor hours (Y).\n\n### Brand Preference\n#### A dataset from the textbook Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, \u0026 Li\nThis [dataset](http://users.stat.ufl.edu/~rrandles/sta4210/Rclassnotes/data/textdatasets/KutnerData/Chapter%20%206%20Data%20Sets/CH06PR05.txt) is based on the following: In a small-scale experimental study of the relation between degree of brand\nliking (Y) and moisture content (X1) and sweetness (X2) of the product, the following results\nwere obtained from the experiment based on a completely randomized design (data are coded).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrantgasser%2Fcomplete-multiple-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgrantgasser%2Fcomplete-multiple-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrantgasser%2Fcomplete-multiple-regression/lists"}