STATS 326作业代写、R实验作业代做、代写R编程设计作业、代做Applied Time Series作业代写留学生Prolog|帮做Haskell程序

STATS 326Applied Time SeriesASSIGNMENT THREEDue: 2 May 2019, 11.00 am(Worth 6% of your final grade)Hand-in to the appropriate STATS 326 Hand-in box in the Student Resource CentreThis assignment will be marked out of 100. Please follow the instructions carefully. Markswill be deducted if you include R output, plots etc that are not asked for. Only include what isrequested in each question in your answers. You are encouraged to print your assignment “2-up” to save paper.The data for this assignment is the same as the data used in Assignment Two.NOTE: Given what was found in Assignment Two with respect to the variables needed forthe best predicting Seasonally Adjusted model of the CO2 Concentration data, youshould be able to fit appropriate final models (without going through any modelbuilding steps) for Questions One and Two.Question One: [20 marks]Build a Seasonal Factor model of the data (2000 to 2016). See pages 90 – 96 of the CourseNotes. Calculate predictions for the 4 quarters of 2017 using your final model. Compare themodel’s forecasts with the actual values for 2017.In your assignment only include the following for the best predicting Seasonal Factormodel: the R summary output for the best predicting model, the R commands and outputused to do the predictions and the R commands and output used to compare the predictionswith the actual values for 2017. Briefly comment on the model.Question Two: [25 marks]Find the best predicting Harmonic model of the data (2000 to 2016). See pages 97 – 114 ofthe Course Notes.In your assignment only include the following for the best predicting Harmonic model: theR summary output for the best predicting model, the R commands and output used to do thepredictions and the R commands and output used to compare the predictions with the actualvalues for 2017. Briefly comment on the best predicting model. Briefly discuss the otherHarmonic models that you tried and briefly ex-plain why they were rejected.For Questions Three and Four, use the best predicting model from Questions One andTwo.Question Three: [30 marks]Write up a brief set of Technical Notes for the best predicting model. You do not need todiscuss any model building steps. You should also discuss the predictions and theirreliability.Question Four: [20 marks]Re-run the best predicting model using all the available data (2000 to 2017) and dopredictions for the 4 quarters of 2018. You are not required to do any model building in thisquestion. Just use the best predicting model from Questions One and Two.In your assignment only include the R commands and output for the best predicting modeland the R commands and output for the 2018 predictions. Briefly comment on the model.Question Five: [5 marks]Which is the best predicting model from Assignments Two and Three? Justify your choice. 本团队核心人员组成主要包括BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：[email protected] 微信：codehelp