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1. Project Overview
In this project you will analyse and explore a large data set based on the daily closing stock-prices of 28 large companies. In particular, you will build and assess linear regression models that explain variability in the daily stock price of Vodafone using stock price data from other companies.
You will be expected to use your own judgement, as well as the course content in order to come up with a predictive model for the Vodafone stock price.
2. Getting Started
The dataset lse looks at the closing share prices for Vodafone and 27 other companies in the FTSE (Financial Times Stock Exchange) 100 Index. The FTSE 100 Index lists the share prices of the 100 companies with the highest market capitalisation that are part of the London Stock Exchange. That is, the companies with the highest market value, worked out by multiplying the company’s share price with the number of shares.  The data were taken from Yahoo Finance and the response variable, which predictions will be made on, is labelled VOD. The dataset includes daily data from January 2016 to January 2019. The other 27 company variables have been standardised with mean 0 and variance 1. The share price for Vodafone is one day ahead and so a regression model can be fit to predict the closing share prices at the end of day (i+1) using those of the 27 companies at the end of day (i)
The data have now been loaded and are accessible in a data frame called lse. You can quickly visualise the data columns by printing the first few rows of data using the head() function
Note that you are being assessed on your approach to the analysis rather than having the perfect model, so make sure that you discuss your analysis as fully and clearly as possible.
3. Research Questions Explore the data (15 Marks)
• Assess the validity of your model assumptions • Discuss the selection of your final model
Discussion of Model (5 Marks)
• Test your model using the prediction tool provided
• Discuss the fit of your model with respect to the validation data given by the prediction tool
4. Report Structure, Content & Submission
Your project report will be graded out of 15 marks based on the rubric available on the course MyPlace page. However, your report should adhere to the following guidelines:
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