# The given dataset, “RedWine.txt”, is used to model wine quality based on physicochemical tests The dataset provides the 1,599 red wine samples from the north of Portugal: International Financial management Assignment, UOW, Australia

 University University of Wollongong (UOW) Subject International Financial management

Red wine quality Dataset

The given dataset, “RedWine.txt”, is used to model wine quality based on physicochemical tests. The dataset provides the 1,599 red wine samples from the north of Portugal. It is a modified version of the data used in the study . This dataset includes 5 variables, denoted as X1, X2, X3, X4, X5, and Y, described as follows:

X1 – citric acid
X2 – chlorides
X3 – total sulphur dioxide
X4 – pH
X5 – alcohol
Y – quality (score between 0 and 10)

1. Understand the data
(i) Import the txt file (RedWine.txt) and save it to your R working directory.
(ii) Assign the data to a matrix, e.g. using
(iii) The variable of interest is quality (Y). To investigate Y, generate a subset of 440 data, e.g. using:
(iv) Using scatter plots and histograms to understand the relationship between each of the variables X1, X2, X3, X4, X5, and the variable of interest Y.

2. Transform the data

Choose any four from the five variables (X1, X2, …, X5). Make appropriate transformations to the chosen four variables and the variable of interest Y individually, so that the values can be aggregated in order to predict the variable of interest. Assign your transformed data along with your transformed variable of interest to an array.

3. Build models and investigate the importance of each variable
(i) Import the AggWaFit718.R file to your working directory and load into the R workspace using,
(ii) Evaluating the following fitting functions on the transformed data:
• A-weighted arithmetic mean (WAM)
• Weighted power means (WPM) with P=2
• An ordered weighted averaging function (OWA)

4. Use your model for prediction

Using your best fitting model based on Q3, predict the wine quality for the input: X1=1; X2= 0.075; X3=41; X4=3.53; X5=9.3

5. Summarising your data analysis procedures in up to 20 slides for a 5-minutes presentation. The slides should include the following contents:

– What kinds of data distribution you have identified in the raw data.
– Explain the transformations applied for the selected four variables and the variable of interest.
– Include two tables – one with the error measures and correlation coefficients, and one summarising the weights/parameters and any other useful information learned for your data.
– Explain the importance of each of the variables (the four variables that you have selected).
– Which fitting function is the best fitting model for your selected data.
– Give your prediction result and comment on whether you think it is reasonable.
– Discuss the best conditions (in terms of your chosen four variables) under which a higher quality wine will occur.

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