MATH3821讲解、辅导Modelling、Java，c++，Python程序语言讲解 辅导Python程序|讲解R语言程序
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SCHOOL OF MATHEMATICS AND STATISTICS
MATH3821 Statistical Modelling and Computing
Term Two 2020
Assignment Two
Given: Friday 17th July 2020 Due date: Sunday 2nd August 2020
INSTRUCTIONS: This assignment is to be done collaboratively by a group of 5 students. The same
mark will be given for the report to each student within the group, unless I have good reasons to believe that
somebody did not do anything.
You will need to produce and submit a report of your work in PDF format. This report will not contain more
than 10 pages, excluding the Appendix that should contain your computing codes. The report is due 11:59
pm, Sunday 2nd August. The first page of this PDF should be this page. Only one of the five students
should submit the PDF file on Moodle, with the names of the other students in the group clearly indicated in
the document.
I/We declare that this assessment item is my/our own work, except where acknowledged, and has not been
submitted for academic credit elsewhere. I/We acknowledge that the assessor of this item may, for the
purpose of assessing this item reproduce this assessment item and provide a copy to another member of the
University; and/or communicate a copy of this assessment item to a plagiarism checking service (which may
then retain a copy of the assessment item on its database for the purpose of future plagiarism checking). I/We
certify that I/We have read and understood the University Rules in respect of Student Academic Misconduct.
Name Student No Signature Date
1
Every other year, at the Joint Statistical Meetings, the Graphics Section and the Computing Section join in
sponsoring a special Poster Session called The Data Exposition , but more commonly known as The Data
Expo. All of the papers presented in this Poster Session are reports of analyses of a common data set provided
for the occasion. In addition, all papers presented in the session are encouraged to report the use of graphical
methods employed during the development of their analysis and to use graphics to convey their findings.
For your assignment 2, you are asked to explore one of these data sets (http://stat-computing.org/dataexpo/)
using one or several of the statistical modelling approaches presented in MATH3821. You might consider
using simulation strategies to investigate the appropriateness of assumptions you will make, or to provide
accuracies of your estimators (e.g., via the Bootstrap).
You should address the following in your written report:
• Goal of your statistical analysis: depending on the problem you decide to work on (and you can
formulate any question you think can be adressed with the data you chose), state clearly what the goal
of your analysis is (i.e., what question(s) you are trying to answer).
• Data collection and exploratory analysis: how did you collect the data. What exploratory analyses did
you do, graphical or otherwise. Which variables do you choose to use in your analysis and why?
• Model choice: what models did you fit? what assumptions did you made on the data generating process?
• Model fitting: what software did you use? how is variable/model selection carried out. Provide any
relevant computing code to support your results.
• Diagnostics: What model checking diagnostics did you carry out if any? What are the limitations if
any.
• Model assessment: How would you assess how useful/good your model is at prediction (if any)? what
are the limitations of your model? You don’t actually need to carry out any formal assessment, a
description of what you can do is enough.
2
SCHOOL OF MATHEMATICS AND STATISTICS
MATH3821 Statistical Modelling and Computing
Term Two 2020
Assignment Two
Given: Friday 17th July 2020 Due date: Sunday 2nd August 2020
INSTRUCTIONS: This assignment is to be done collaboratively by a group of 5 students. The same
mark will be given for the report to each student within the group, unless I have good reasons to believe that
somebody did not do anything.
You will need to produce and submit a report of your work in PDF format. This report will not contain more
than 10 pages, excluding the Appendix that should contain your computing codes. The report is due 11:59
pm, Sunday 2nd August. The first page of this PDF should be this page. Only one of the five students
should submit the PDF file on Moodle, with the names of the other students in the group clearly indicated in
the document.
I/We declare that this assessment item is my/our own work, except where acknowledged, and has not been
submitted for academic credit elsewhere. I/We acknowledge that the assessor of this item may, for the
purpose of assessing this item reproduce this assessment item and provide a copy to another member of the
University; and/or communicate a copy of this assessment item to a plagiarism checking service (which may
then retain a copy of the assessment item on its database for the purpose of future plagiarism checking). I/We
certify that I/We have read and understood the University Rules in respect of Student Academic Misconduct.
Name Student No Signature Date
1
Every other year, at the Joint Statistical Meetings, the Graphics Section and the Computing Section join in
sponsoring a special Poster Session called The Data Exposition , but more commonly known as The Data
Expo. All of the papers presented in this Poster Session are reports of analyses of a common data set provided
for the occasion. In addition, all papers presented in the session are encouraged to report the use of graphical
methods employed during the development of their analysis and to use graphics to convey their findings.
For your assignment 2, you are asked to explore one of these data sets (http://stat-computing.org/dataexpo/)
using one or several of the statistical modelling approaches presented in MATH3821. You might consider
using simulation strategies to investigate the appropriateness of assumptions you will make, or to provide
accuracies of your estimators (e.g., via the Bootstrap).
You should address the following in your written report:
• Goal of your statistical analysis: depending on the problem you decide to work on (and you can
formulate any question you think can be adressed with the data you chose), state clearly what the goal
of your analysis is (i.e., what question(s) you are trying to answer).
• Data collection and exploratory analysis: how did you collect the data. What exploratory analyses did
you do, graphical or otherwise. Which variables do you choose to use in your analysis and why?
• Model choice: what models did you fit? what assumptions did you made on the data generating process?
• Model fitting: what software did you use? how is variable/model selection carried out. Provide any
relevant computing code to support your results.
• Diagnostics: What model checking diagnostics did you carry out if any? What are the limitations if
any.
• Model assessment: How would you assess how useful/good your model is at prediction (if any)? what
are the limitations of your model? You don’t actually need to carry out any formal assessment, a
description of what you can do is enough.
2