Case Studies M.Sc. in Applied Statistics

Report
Case Studies
M.Sc. in Applied Statistics
Dr. Órlaith Burke
Michaelmas Term 2013
Linear Models
M.Sc. in Applied Statistics
Dr. Órlaith Burke
Michaelmas Term 2013
Autocorrelation
M.Sc. in Applied Statistics
Dr. Órlaith Burke
Michaelmas Term 2013
Statistical Methods
M.Sc. in Applied Statistics
Statistical Methods
M.Sc. in Applied Statistics
Hard work
Statistical Methods
M.Sc. in Applied Statistics
Hard work = Stay engaged
Statistical Methods
M.Sc. in Applied Statistics
Hard work = Stay engaged
Responsibility
Statistical Methods
M.Sc. in Applied Statistics
Hard work = Stay engaged
Responsibility = Ownership
Statistical Methods
M.Sc. in Applied Statistics
Hard work = Stay engaged
Responsibility = Ownership
Read
Statistical Methods
M.Sc. in Applied Statistics
Hard work = Stay engaged
Responsibility = Ownership
Read = Read
Case Studies
‘Other’ skills
Transferable skills
Case Studies
Presentation skills
Case Studies
Presentation skills
Groupwork
Case Studies
Presentation skills
Groupwork
Critical Thinking
Case Studies
Presentation skills
Groupwork
Critical Thinking
Synthesis of Ideas
Case Studies
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Logistics
Aims of the course
Tasks
Structure of the Lectures
Presentation types
Feedback
Personal Reflection
Groups: Allocation, tasks and group work
Logistics
11 am Friday morning Lecture Room in SPR1
Week 1-6 MT
10.30am Weeks 7-8 MT
No lecture in Week 2
Lecture Room also available 10.30-11am on Friday mornings for
presenting groups to SET UP and practise before the session that day.
Please note that lectures will start promptly on time!
Material available on WebLearn
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Aims of the Course
The aims of the Case Studies module are:
• to broaden participants’ exposure to practical aspects of statistics;
• to develop in participants a critical awareness of how statistical
ideas and techniques are used in practice;
(See ‘Scientific Thinking’ excerpt on class page)
• to improve the participants’ skill in formulating and delivering a
presentation on a statistical topic; and
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• to broaden participants’ exposure to practical aspects of statistics;
• to develop in participants a critical awareness of how statistical
ideas and techniques are used in practice;
(See ‘Scientific Thinking’ excerpt on class page)
• to improve the participants’ skill in formulating and delivering a
presentation on a statistical topic; and
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• to develop in participants a critical awareness of how statistical
ideas and techniques are used in practice;
(See ‘Scientific Thinking’ excerpt on class page)
• to improve the participants’ skill in formulating and delivering a
presentation on a statistical topic; and
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• to develop in participants a critical awareness of how statistical
ideas and techniques are used in practice;
(See ‘Scientific Thinking’ excerpt on class page)
• to improve the participants’ skill in formulating and delivering a
presentation on a statistical topic; and
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• DEVELOPMENT OF CRITICAL THINKING AND PRACTICALITIES OF
STATISTICS IN THE REAL WORLD
• to improve the participants’ skill in formulating and delivering a
presentation on a statistical topic; and
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• DEVELOPMENT OF CRITICAL THINKING AND PRACTICALITIES OF
STATISTICS IN THE REAL WORLD
• to improve the participants’ skill in formulating and delivering a
presentation on a statistical topic; and
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• DEVELOPMENT OF CRITICAL THINKING AND PRACTICALITIES OF
STATISTICS IN THE REAL WORLD
• DIRECTLY TRANSFERABLE SKILLS
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• DEVELOPMENT OF CRITICAL THINKING AND PRACTICALITIES OF
STATISTICS IN THE REAL WORLD
• DIRECTLY TRANSFERABLE SKILLS
• to encourage the development of participants’ group work skills.
Aims of the Course
Why?
The aims of the Case Studies module are:
• SO THAT YOU BECOME WELL-ROUNDED STATISTICIANS
• DEVELOPMENT OF CRITICAL THINKING AND PRACTICALITIES OF
STATISTICS IN THE REAL WORLD
• DIRECTLY TRANSFERABLE SKILLS
• DIRECTLY TRANSFERABLE SKILLS
AND
PRACTICE FOR ASSESSED GROUP PRACTICAL
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Tasks
Each student will:
• study 1 case, for main presentation, in randomly allocated
groups of 4-5;
• be assigned to speak for part of one presentation (at least);
• study 1 case, for debate-style presentation, in randomly
allocated groups of 2-3;
• study cases for question rounds, in randomly allocated groups of
1-2;
• produce a short Personal Reflection for each group work task.
Please note that finding papers in the Oxford library system
is part of the task.
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Structure of Lectures
The lectures for this course will be as follows:
– Seminar-style presentation of main case study
– followed by ‘quick-fire’ questions
– followed by active discussion from the audience
– Debate-style presentation of second case study
– followed by active discussion from the audience
Structure of Lectures
The lectures for this course will be timed as follows:
– Seminar-style presentation of main case study 15 minutes
– followed by ‘quick-fire’ questions 10 minutes
– Debate-style presentation of second case study 5 minutes each
– followed by active discussion from the audience 5 minutes
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Seminar-Style Presentations
• Every member of the group should contribute
significantly (but not necessarily equally) to the
presentation.
• You are not expected to analyse the data yourselves, and
please do not discuss the theory - this should be a study
of examples.
Seminar-Style Presentations
A good presentation will:
• clearly and efficiently communicate the context, aims,
methods and findings of the reported analysis;
• deliver a clear, fair and accurate critique of the reported
analysis;
• suggest alternative analyses or ideas for improvement if
appropriate; and
• deal appropriately and constructively with questions and
feedback from the audience.
See ‘Presentation hints’ on the class page
Seminar-Style Presentations
• Remember that the audience will not have seen the report.
• Common mistake:
Groups do not clearly introduce the problem or describe the
context of the analysis.
This makes the rest of the presentation almost impossible to
follow.
• Technology:
You can make use of the computer projector but do prepare in
advance, e.g. have the file on your desktop, and log on to the
computer before the talk
(it takes much longer to log-in the first time you use it!).
Debate-Style Presentations
• Each debate group will present the positive OR negative
aspects (as assigned) of the case study
• These are 5 minute short key point presentations
• Every member of the group should contribute significantly
(but not necessarily equally) to the presentation.
Quick-Fire Question Rounds
• Lead by the ‘quick-fire’ question group after the presentation.
• ‘Quick-fire’ question group members will
– be familiar with the case study
– focus on positive OR negative aspects of the study (as assigned)
– have a few short (but interesting) questions for the main
presentation group.
• The idea is not to try to catch the presentation group out
but to encourage active discussion of the case study.
• ‘Quick-fire’ question rounds will develop into the general questions
and discussion with the rest of the audience.
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Feedback
• At the end of the lecture (as you leave)
• Post-it anonymous feedback
All students will have an opportunity to receive
individual feedback on their presentation.
General feedback also given to each group after the
session (e.g. statistical key points and feedback on
presentation/team work)
Feedback
Feedback is valuable
– It helps you to assess your performance, and to improve.
However, please keep the following in mind:
• Feedback should be positive or constructive, but never
negative.
• Feedback should not be personal - you are communicating
your perception of the presentation, not making a value
judgement about an individual.
• Please consult feedback notes online before the first session
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Groups
Groupwork
• Each individual will bring a different set of skills and background
knowledge to the group.
• You will therefore get the most out of the exercise if you work
together to prepare the presentation.
• Make sure every meeting has a focus, and that each individual is
given the chance to contribute their ideas and skills.
• You may find it useful at first to assign someone the role of
chairman, in order to make sure that the discussion stays on track.
Remember this is all practice for the Assessed Group Practical
Groups
Allocation
Students will be assigned to randomly allocated groups for
• Main case study Seminar-Style presentation – Groups of 4-5
• Second case study Debate-Style presentation – Groups of 3-4
• Quick-fire Question Groups for Main case study – Groups of 2-3
• The references for case studies to be discussed will be available
one week before each session
Groups
Tasks
Each main presentation group will:
• formulate and deliver a 15 min presentation on a particular
case;
• be prepared for ‘quick-fire’ questions and general questions
from the audience; and
• produce a team plan – to be submitted no later than 24 hours
before presentation.
Groups
Tasks
Each debate presentation group will:
• formulate and deliver a 5 min presentation on a particular case;
• be prepared to defend their comments; and
• produce a team plan – to be submitted no later than 24 hours
before presentation.
Groups
Tasks
Each ‘quick-fire’ question group will:
• briefly review the case to be presented; and
• prepare 1-2 interesting questions for discussion with the
presentation group.
Online Material
Aims of the course
Tasks
Structure of the lecture
Presentation hints and tips
Feedback
Groups
Team Plan and Personal Reflection
Team Plan & Personal Reflection
Each presentation group will submit a TEAM PLAN no less
than 24 hours before the presentation
(i.e. By 11am Thursday)
The TEAM PLAN will outline the individual roles of each
student in the group
e.g. presenter, slide writer ...
Team Plan & Personal Reflection
Each student will produce a short Personal Reflection for
each group work task in which they are involved
To be submitted by 2pm Monday morning
By Week 8 each student should have three Personal Reflections
A Personal Reflection should be
• an individual piece of writing
• no more than a single page
• a brief description of your own experience of a particular
groupwork task
Linear Models
M.Sc. in Applied Statistics
Dr. Órlaith Burke
Michaelmas Term 2013
Course Overview
1.
2.
3.
4.
5.
Linear Regression
Multiple Linear Regression
Prediction and Residual Diagnostics
Resistant Regression
Classical Applications to ANOVA
Course Overview
Breakdown:
6 Lectures
1 Assignment
1 Class
2 Practicals:
1 of which is an Assessed Practical
Autocorrelation
M.Sc. in Applied Statistics
Dr. Órlaith Burke
Michaelmas Term 2013
Course Overview
1.
2.
3.
4.
5.
Basics and Introduction
Identification and Estimation
Non-Stationarity
Diagnostics and Forecasting
Decomposition and MCMC Output
Course Overview
Breakdown:
4 Lectures
1 Assignment
1 Class
2 Practicals:
1 of which is an Assessed Practical
Course Overview
Lecture Slides
Notes
Reading
Lecture time will be aimed at discussion
of topics and examples
Reading will be required!

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