这个project是调用饮酒行业,并撰写报告
School of Mathematics
3AS/3AS4: Applied Statistics
Data Analysis Project
2019-2020
Wine industry shows a recent growth spurt as social drinking is on the rise. The
price of wine depends on a rather abstract concept of wine appreciation by wine tasters.
Pricing of wine depends on such a volatile factor to some extent. Another key factor in
wine certification and quality assessment is physicochemical tests which are laboratorybased and takes into account factors like acidity, pH level, presence of sugar and other
chemical properties. For the wine market, it would be of interest if human quality of
tasting can be related to the chemical properties of wine so that certification and quality
assessment and assurance process is more controlled. Two data sets are available from
https://archive.ics.uci.edu/ml/datasets/Wine+Quality of which one data set is on
red wine and have 1599 different varieties and the other is on white wine and has 4898
varieties. All wines are produced in a particular area of Portugal. Data are collected on
11 different properties of the wines based on chemical including density, acidity, alcohol
content etc. All chemical properties of wines are continuous variables. The last column
is quality, which is an ordinal variable with possible ranking from 1 (worst) to 10 (best).
Each variety of wine is tasted by three independent tasters and the final rank assigned is
the median rank given by the tasters. Details of the variables involving chemical properties
can be obtained from the data website.
In this project, you may consider both red and white wines or only red or only white
wines. Main objective is to build a model to predict the quality of the wine based on its
physiochemical properties. Some suggested guidelines are as follows:
1. Download the data sets from the website. It is a big data set and so physical checking
of errors is nearly impossible.
2. If you are using both types of wines, merge the data sets into one with a column
indicating the wine type (red or white).
3. As a preliminary step, check if the properties of these wines differ for red and white
wines or for good quality and poor quality wines. You may compare the means
using proper tests and/or you may use visualization tools.
4. Create training and test data sets, by making some random partitions.
5. You may use linear regression to predict the quality of the wines. Remember to use
model selection techniques to choose the best model and check for the assumptions
of the linear regression.
6. You may create a dummy variable indicating good wines and poor wines based on
the quality and then use classification techniques to predict the quality of the wines.
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In a preprocessing step, you may like to scale all variables. Report how do you
choose parameters of your classification technique. What are the training and test
error rates?
7. You may also use some unsupervised learning methods like cluster analysis to see
if you can cluster the wines based on their chemical properties. Do these clusters
correspond with their quality or the wine type?
You need to submit a report not exceeding 10 pages in a single pdf file, which should
have the following structure:
• Introduction
• Pre-processing
• Data Analysis
• Conclusion
• References
All suggestions for data analysis are suggestions only. You are free to use whatever
you like to explore the main problem. Extra credits will be given for comparing more
than one method. All results should be presented in proper tables or plots. Copy/pasting
R output is considered as a poor presentation. Proper references should be cited in the
text. You don’t need to provide any R code.
Marking of the projects will be based on the following:
• Presentation. Presentation of the report should be in a technical writing form.
You do not need to include mathematical details, but the methods used should
be properly mentioned. You must also have a proper structure of a presentation.
Tables and plots should be as scientific looking as possible. For examples, look into
research papers on data analysis.
• Data Analysis. Correctness and logical development of the analysis is the most important part of the project. Innovative approaches will get extra credit. Everything
should be clearly documented so that one can reproduce the analysis.
• Conclusion. You must have concluding remarks about your project, stating what
is the final model, whether there are any limitations to your analysis, any further
analysis, difficulties faced etc.
• References. There must be some proper references on the methods, data source,
or any part of your project you think it is necessary.
All projects need to be submitted in Canvas and they will be checked for plagiarism.
Suspected cases will be investigated further and may lead to serious penalties including a
fail in the whole module.
Please feel free to consult me for any aspect of the project.
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