Econometrics and Forecasting Workbook 1 This is one of two workbooks you are asked to complete for the EF308 module. This workbook is worth 50% of the full module grade and relates to material covered in Part 1 (Working with Data) and Part 2 (Regression as Knowing) of the module. You are asked to complete the workbook, including the written responses, in a Python document. See the accompanying video for how to do this. When you are finished the workbook, you should do the following: •Download the .ipynb file (the python file)•Print to PDF the .ipynb file with every cell run•Upload both files to the Assignment 1 part of LoopA big part of using Python effectively is to use codes that others have developed, so as not to duplicateefforts. You are, therefore, allowed touse code from elsewhereas part of answering this assignment, but for the purposes of fair assessment there are two restrictions on this:-Please do not use code developed by other people in your class-All words used in the writing parts must be your ownAlso try not to ‘over-answer’the workbook.The total writing should be about 1500 words and theprint-to-pdfPDF no more than about 10 pageslong. These are not hard rules, but try not to exceedthis too much. A good answer should be quitecomprehensive, but also not needlessly wordy.The assessmentFor the dataset, HousePrices.csv, load the dataset in Python and answer the following questions / requests, including showing all code used:
1.Describe the dataset using any appropriate descriptive statistics and visuals. [30 marks]
2.Develop a theoretical model, based on prior research, of how house prices are determined based on house features. [20 marks]. This should be no more than about 500 words in writing and no more than about 5 references.
3.Test, using linear regression, your proposed theoretical model to explain house prices and analyse the findings. [20 marks]. It may be necessary, or helpful, to transform some of the variables in the dataset before running these tests.
4.Run all suitable diagnostic tests on the regression specification, making improvements to the regression specification where necessary. Discuss the issues and changes made. [30 marks].