Aggregation Functions for Data Analysis - European Stockmarket Prediction Dataset - Data Analysis Assignment Help

Assignment Task

Using aggregation functions for data analysis
The provided zip file contains the data file [EuStockMarkets2.txt ] and the Rcode [AggWaFit718.R ] to use with the following tasks. Include these in your R working directory.

European Stockmarket Prediction Dataset
The given dataset, ”EuStockMarkets2.txt”, can be used to create models of prediction of stockmarkets. The dataset provides the values of European stockmarket indices using 1200 measurements. The dataset includes 4 variables, denoted as X1, X2, X3 and X4, described as follows:

1-X1- DAX The blue chip stock market index that comprises 30 major German companiestrading on the Frankfurt Stock exchange and is also called DAX30. The index carries similar weight as Dow Jones in US and FT30.

2-X2 – SMI The Swissm Market Index (SMI)] is Switzerland’s blue-chip stock market indexwhich makes it the most followed in the country. It is made up of the 20 of the largestand the most liquid Swiss Performance Index (SPI) stock. As a price index, the SMI is not adjusted for dividends.

3-X3-CAC The French CAC40 (Cotation Assiste´e en Continu) is a benchmark French stockmarket index. The index represents a capitalisation-weighted measure of the 40 most
significant among the 100 largest market caps on the Euronext Paris, formerly the Pari Burse.

4-X4-FTSE The Financial Times Stock Exchange Group (FTSE) is an independent organisation. It specialises in creating index offerings for the global financial markets. An index
will represent a market segment and its hypothetical portfolio of stock holdings. The variable X4-FTSE is the target variable

1. Understand, clean (if necessary) and prepare the data 
(i) Download the txt file (EuStockMarkets2.txt) from CloudDeakin and save it to your R working directory.
(ii) Assign the data to a matrix, e.g. using the.data <- as.matrix(read.table("EuStockMarkets2.txt"))
(iii) Your variable of interest is X4, the FTSE index. Generate a subset of 500 data, e.g. using: my.data <- the.data[sample(1:1200,500),c(1:4)]
(iv) Using descriptive statistics, find the main statistical characteristics of all variables.
Include assessment of the the outliers using boxplots (4 boxplots). Record your results in a table (Table 1).

(v) Using scatterplots and histograms, report on the general properties of each variable Xi, i=1,2,3,4 and relationship (if any) between each of the variables X1,X2, X3andyour variable of interest X4 (FTSE). Include a scatter plot for each of the variables Xi,i=1,2,3,4, (4 scatterplots) and each pair of variables, e.g. X1 and X4, X1 and X2, etc.(6 scatterplots). Include a histogram for each variable X1,X2,X3 and X4 (4 histograms). Include 1 or 2 sentences about the relationships and distributions.

2. Transform the data 
(i) Apply transformations to all variables, X1, X2, X3, to model X4.Make appropriate transformations to the variables so that the values can be aggregatedin order topredict the variable of interest X4 (FTSE). The transformations should reflectthe general relationship between each of the four variables and the variableofinterest.Assign your transformed data along with your transformed variable of interest to an array (it should be 500 rows and 4 columns). Save it to a txt file titled ”name-transformed.txt”using write.table(your.data,"name-transformed.txt",)

(ii) Briefly explain each transformation for all variables. You may also include mathematical formulae if appropriate (1- 2 sentences each).
(iii) Include the histograms of all four transformed variables in your report (4 histograms).

This Data Analysis Assignment has been solved by our Data Analysis Experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.

Be it a used or new solution, the quality of the work submitted by our assignment Experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.