{"id":8817,"date":"2020-11-18T22:18:52","date_gmt":"2020-11-18T22:18:52","guid":{"rendered":"http:\/\/onlineclassesguru.com\/?p=8817"},"modified":"2020-11-18T22:18:52","modified_gmt":"2020-11-18T22:18:52","slug":"predict-the-number-of-cyclists-on-the-galloping-goose-trail","status":"publish","type":"post","link":"https:\/\/onlineclassesguru.com\/index.php\/2020\/11\/18\/predict-the-number-of-cyclists-on-the-galloping-goose-trail\/","title":{"rendered":"Predict the number of cyclists on the Galloping Goose trail"},"content":{"rendered":"<style type=\"text\/css\"><\/style><p>The task is to predict the number of cyclists on the Galloping Goose trail from Wednesday Nov 20th to<br \/>\nTuesday Nov 26th (inclusive).<br \/>\n1. Write a short report describing your model and resulting forecasts.<br \/>\nA good report would:<br \/>\ni. Have a clear structure, such as:<br \/>\na. Introduction: introduce the forecasting problem.<br \/>\nb. Data: describe the dataset, any additional variables you may be using, any apparent<br \/>\npatterns, trends etc. including any relevant references.<br \/>\nc. Methods: describe your forecasting model, ideally including an equation that shows<br \/>\nyour model. Carefully specify what variables are included and why you include them.<br \/>\nd. Results: if applicable show your in-sample estimated model as a table<br \/>\ne. Conclusion: summarise your results, discuss how your forecasting model could be<br \/>\nimproved.<\/p>\n<p>If you present regression results these should either be in the form of an equation<br \/>\n(as we have seen in lecture slides) or as a table, and include coefficient estimates,<br \/>\nstandard errors, and the number of observations.<br \/>\nBe clearly referenced. Make sure to include references to any relevant datasets or<br \/>\nliterature.<br \/>\nNot exceed 2 pages in length (excluding references).<\/p>\n<p><center><a href=\"http:\/\/onlineclassesguru.com\/orders\/ordernow\"><img decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcTyj99p60XCLyLk1htB7-1neRt8-2QdnenNlQ&usqp=CAU\"target=\"_http:\/\/onlineclassesguru.com\/orders\/ordernow\"\/><\/center><p>","protected":false},"excerpt":{"rendered":"<p>The task is to predict the number of cyclists on the Galloping Goose trail from Wednesday Nov 20th to Tuesday Nov 26th (inclusive). 1. Write a short report describing your model and resulting forecasts. A good report would: i. Have a clear structure, such as: a. Introduction: introduce the forecasting problem. b. Data: describe the&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-8817","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Predict the number of cyclists on the Galloping Goose trail - onlineclassesguru<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/onlineclassesguru.com\/index.php\/2020\/11\/18\/predict-the-number-of-cyclists-on-the-galloping-goose-trail\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predict the number of cyclists on the Galloping Goose trail - onlineclassesguru\" \/>\n<meta property=\"og:description\" content=\"The task is to predict the number of cyclists on the Galloping Goose trail from Wednesday Nov 20th to Tuesday Nov 26th (inclusive). 1. Write a short report describing your model and resulting forecasts. A good report would: i. Have a clear structure, such as: a. Introduction: introduce the forecasting problem. b. 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