MATH20811程序辅导、R留学生编程讲解、data analysis程序辅导讲解留学生Processing|辅导Python程序

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Coursework 1 – Exploratory data analysis and correlation
MATH20811 Practical Statistics: Coursework 1
(November 2020)
The marks awarded for this coursework constitute 30% of the total assessment for the module.
Your solution to the coursework should be fairly concise (maximum of about 10 pages) and it
should take, on average, about 15 hours to complete.
Please read all the instructions and advice given below carefully.
The submission deadline is 10:00 am on Monday 16 November 2020.
Late Submission of Work: Any student’s work that is submitted after the given deadline will
be classed as late, unless an extension has already been agreed via mitigating circumstances or a
DASS extension.
The following rules for the application of penalties for late submission are quoted from the
University guidance on late submission document, version 1.3 (dated July 2019):
”Any work submitted at any time within the first 24 hours following the published submission
deadline will receive a penalty of 10% of the maximum amount of marks available. Any work
submitted at any time between 24 hours and up to 48 hours late will receive a deduction of 20%
of the marks available, and so on, at the rate of an additional 10% of available marks deducted
per 24 hours, until the assignment is submitted or no marks remain.”
Your submitted solutions should all be in one document. This must be prepared using LaTeX.
For each part of the question you should provide explanations as to how you completed what is
required, show your workings and also comment on computational results, where applicable.
When you include a plot, be sure to give it a title and label the axes correctly.
When you have written or used R code to answer any of the parts, then you should list this R code
after the particular written answer to which it applies. This may be the R code for a function you
have written and/or code you have used to produce numerical results, plots and tables. R code
should also be clearly annotated.
Do not use screenshots of R code/output. Instead, to include R code use the verbatim environment
and summarise R output in tables using the table environment, as demonstrated in the solution of
Example Sheet 2.
Your file should be submitted through the module site on Blackboard to the Turnitin assessment
in the Coursework folder entitled “MATH20811 CW1” by the above time and date. The work
will be marked anonymously on Blackboard so please ensure that your filename is clear but that
it does not contain your name and student id number. Similarly, do not include your name and
id number in the document itself.
There is a basic LaTeX template file on Blackboard which you may choose to use for typing-up
your solutions. The file is called CW1_submitted_work.tex.
Coursework 1 – Exploratory data analysis and correlation
Turnitin will generate a similarity report for your submitted document and indicate matches to
other sources, including billions of internet documents (both live and archived), a subscription
repository of periodicals, journals and publications, as well as submissions from other students.
Please ensure that the document you upload represents your own work and is written in your own
words. The Turnitin report will be available for you to see shortly after the due date.
This coursework should hopefully help to reinforce some of the methodology you have been studying,
as well as the skills in R you have been developing in the module. Correct interpretation and
meaningful discussion of the results (i.e. attempt to put the results into context) are as important
as correct calculation of the results, in order to achieve a high mark for the coursework.
Coursework 1 – Exploratory data analysis and correlation
The data are in the file white_wine.csv (Cortez et al, 2009) contain various measurements on
white wine variants of the Portuguese Vinho Verde wine. Import the data into R from your default
folder using the command:
white_wine=read.table("white_wine.csv", sep = ";", header = TRUE)
The object white_wine contains measurements on 11 continuous variables: fixed.acidity,
volatile.acidity, citric.acid, residual.sugar, chlorides, free.sulfur.dioxide,
total.sulfur.dioxide, density, pH, sulphates, alcohol plus one discrete, ordinal variable:
quality.
For the purposes of this coursework we will just use the variables in columns 7, 8 and 12 which
are:
total.sulfur.dioxide
density
quality
Note that total.sulfur.dioxide and density are both numeric variables, quality is a discrete,
ordinal variable
1. (i) Using selected summary statistics and graphical displays from those discussed in weeks
1 and 2 of this module, explore the univariate empirical distribution of
total.sulfur.dioxide. Comment on your results.
[4]
(ii) Using box-plots, look at the distributions of the total.sulfur.dioxide data at the
different values of quality. Comment on the results.
[4]
(iii) Produce a scatterplot of the total.sulfur.dioxide and density data with the contours
from a bivariate Normal density having appropriately estimated parameters.
Comment on your impression of the bivariate Normal distribution as a suitable probability
model for these data.
[4]
2. Using the function cor, calculate both Pearson’s and Spearman’s correlation between:
❼ total.sulfur.dioxide and density
❼ log(total.sulfur.dioxide) and log(density)
Comment on the results and give an explanation for any discrepancies or similarities between
the various correlation estimates.
[3]
Coursework 1 – Exploratory data analysis and correlation
3. Let ρ1 denote the correlation in the joint distribution of total.sulfur.dioxide and density.
Based on using Pearson’s correlation coefficient, perform a DIY (ie. write your own code to
do the calculations) hypothesis test of
H0 : ρ1 = 0.5 vs HA : ρ1 6= 0.5
at the 5% significance level using Fisher’s Z-transform. Compute the p-value and use it to
decide whether to reject the null hypothesis in favour of the alternative.
Calculate DIY an approximate 95% confidence interval (CI) for ρ1 based on Fisher’s Ztransform
and verify that your calculations agree with the CI produced by cor.test.
[6]
4. Write a function in R to verify via simulation that the distribution of Fisher’s Z-transform
statistic, for a given sample size n, is approximately Normal. Your function should produce
a plot comparing the sampling distribution of Fisher’s Z-transform statistic and the appropriate
approximate Normal distribution the statistic has under the assumption that the true
correlation parameter equals zero. In your simulation, you may assume sample data pairs
(x, y) come from independent Normal distributions having user-input parameter values.
As your solution to this part, please submit the code for your function and also run it in R
to produce the plot described in the paragraph above.
[6]
5. Using the function cor.test, test null the hypothesis:
H0: there is no monotonic association between total.sulfur.dioxide and density in their
joint distribution vs HA: there is a monotonic association between these two variables.
Describe how the test has been carried out for these data and report your conclusions.
[3]
References
[1] P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data
mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553.
ISSN: 0167-9236.

MATH20811程序辅导、R留学生编程讲解、data analysis程序辅导 讲解留学生Processing|辅导Python程序

MATH20811程序辅导、R留学生编程讲解、data analysis程序辅导讲解留学生Processing|辅导Python程序