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STAT271 – Statistics I

2017 – S2 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Associate Lecturer
Suzanne Curtis
12 Wally's Walk (E7A) Room 622
To be determined
Credit points Credit points
3
Prerequisites Prerequisites
STAT272
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This is a unit in statistical methods for the analysis of data in which attention is given to the theoretical structure underlying the techniques. It aims to equip students with a wide understanding of statistics such that they are able to employ appropriate methods of analysis in various circumstances. The techniques learned are widely used in the sciences, social sciences, business and many other fields of study. This unit is designed for students majoring in statistics and/or actuarial studies.
Topics include: inference about one and two sample problems using normal theory and non-parametric methods; analysis of variance; multiple comparisons; and regression.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at http://students.mq.edu.au/student_admin/enrolmentguide/academicdates/

Learning Outcomes

  1. Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  2. Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.
  3. Understand the basic analysis of categorical data (binomial, multinomial and bivariate).
  4. Understand the uses of simple and multiple linear regression.

General Assessment Information

Assignments: Due dates/times and submission details will be specified separately for each assignment. The assignments will be made available at least one week prior to when they are due. Some marks will be allocated for clarity of reasoning and presentation in each assignment.   Late submissions will not be marked (and given a zero mark). Late submissions will only be granted based on an approved "Disruption to Studies" application.

In Class Tests: The tests (of 45 minutes duration) will be held during a lecture in the weeks indicated.  Specific dates are yet to be determined.  Students are permitted to take into the test room one (1) A4 page of personal summary of formulae or notes, written on one or both sides of the page. These notes may be handwritten or typed. You will be permitted to take this sheet with you at the completion of the tests. Some formulae will be provided (as per statistical tables available on iLearn); all necessary statistical tables will be provided.

Examination: The final examination will be of 3 hours duration with 10 minutes reading time. All material covered in the unit is examinable. Relevant statistical tables will be provided at the final examination. These will be the same as the ones made available during the teaching of the unit. For the final examination students will be permitted to take into the exam room a nonprogrammable calculator and two (2) A4 pages of formulae or notes, written on one or both sides of the page. These notes may be hand-written or typed. Students will not be permitted to take these with them at the completion of the exam, and it is recommended that a photocopy be made if a student has a desire to retain them.

Disruption to Studies (final examination): If you notify the University of your disruption to studies for your final examination, you must make yourself available for the week of December 11 – 15, 2017. If you are not available at that time, there is no guarantee an additional examination time will be offered. Specific examination dates and times will be determined at a later date.

Assessment Tasks

Name Weighting Hurdle Due
Assignments 10% Weeks 5 and 12
Test One 15% Week 7
Test Two 15% Week 11
Exam 60% University Examination Period

Assignments

Due: Weeks 5 and 12
Weighting: 10%

Two assignments with specific due dates yet to be determined.

 


This Assessment Task relates to the following Learning Outcomes:
  • Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  • Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.

Test One

Due: Week 7
Weighting: 15%

Class Test One


This Assessment Task relates to the following Learning Outcomes:
  • Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  • Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.

Test Two

Due: Week 11
Weighting: 15%

Class Test Two


This Assessment Task relates to the following Learning Outcomes:
  • Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  • Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.
  • Understand the basic analysis of categorical data (binomial, multinomial and bivariate).
  • Understand the uses of simple and multiple linear regression.

Exam

Due: University Examination Period
Weighting: 60%

Final examination.


This Assessment Task relates to the following Learning Outcomes:
  • Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  • Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.
  • Understand the basic analysis of categorical data (binomial, multinomial and bivariate).
  • Understand the uses of simple and multiple linear regression.

Delivery and Resources

Classes STAT271 is delivered by lectures (3 per week) and tutorials (1 per week, commencing in week 2). All teaching material will be available on iLearn.

Required and Recommended Texts and/or Materials Recommended: Mendenhall W, Wackerly D and Scheaffer R. “Mathematical Statistics with Applications”, Seventh Edition QA276 .M426 2008 Copies of this book are held in Special Reserve in the University Library. The Library also holds copies of the sixth and previous editions as well as the Student solutions manual. The following books are useful references for this unit:

Authors Title Library Call No.
Bain, L.J. & Engelhardt, M Introduction to Probability and Mathematical Statistics QA273.B2546/1992
Conover, W.J. Practical Nonparametric Statistics QA278.8.C65/1999
Hogg, R.V. & Craig, A.T. Introduction to Mathematical Statistics QA276.H59 / 1995
Larson, H.J. Introduction to Probability Theory and Statistical Inference QA273.L352/1982
Walpole, R.E. & Myers, R.H. Probability and Statistics for Engineers and Scientists TA340.W35/1993

 

Unit Schedule

Topic

Description

1

Parameter Estimation:  Point estimation methods, including the method of moments and maximum likelihood. Properties of estimators. Asymptotic (large sample) properties.

2

Sampling distributions:  Properties of and distributions of sample statistics. Definition and derivation of t, F and chi-squared distributions etc.

3

Interval estimation:  Pivotal quantities and confidence intervals.

4

Inference Theory:  Principles of hypothesis testing. Type I and Type II errors. Power. Comparison of competing tests. Relationship between confidence intervals and hypothesis testing.

5

Binomial:  Confidence intervals and hypothesis testing for the probability parameter in the binomial distribution.

6

Single population inference:  Confidence intervals and hypothesis testing (for location and scale), including related samples (paired comparisons). Classical (normal theory) and nonparametric procedures are considered.

7

Two populations inference:  Confidence intervals and hypothesis testing (for location and scale). Classical (normal theory) and nonparametric procedures are considered.

8

Categorical data inference:  goodness of fit tests; tests of association; and tests of homogeneity (chi-squared tests).

9

Correlation and linear regression:  Model fitting and inference for simple and multiple linear regression.

10

k populations inference:  One-way analysis of variance and nonparametric techniques. Multiple comparisons and contrasts.

11

Inference for two factor designs:  normal theory (two-way analysis of variance) and nonparametric techniques. Multiple comparisons and contrasts.

Learning and Teaching Activities

Lectures

Lecture notes will be made available in advance from iLearn.

Tutorials

Tutorial Exercises will be available from iLearn at least five days prior to the tutorial. Students are expected to have attempted these prior to the tutorial. Solutions will be explained, with emphasis on any area students had trouble with. Solutions will then be places on iLearn. on the tutorial exercises or lecture material. Students may attend as many tutorials as desired.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:

Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html

Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html

Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html

Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html​

Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html

Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policies/special-consideration

In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/

Results

Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Enquiry Service

For all student enquiries, visit Student Connect at ask.mq.edu.au

Equity Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

Effective Communication

We want to develop in our students the ability to communicate and convey their views in forms effective with different audiences. We want our graduates to take with them the capability to read, listen, question, gather and evaluate information resources in a variety of formats, assess, write clearly, speak effectively, and to use visual communication and communication technologies as appropriate.

This graduate capability is supported by:

Learning outcomes

  • Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  • Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.
  • Understand the basic analysis of categorical data (binomial, multinomial and bivariate).
  • Understand the uses of simple and multiple linear regression.

Assessment tasks

  • Assignments
  • Test One
  • Test Two
  • Exam

Discipline Specific Knowledge and Skills

Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.

This graduate capability is supported by:

Learning outcomes

  • Understand the concepts of parameter estimation (point and interval), including asymptotic properties.
  • Be able to correctly identify and implement standard normal and nonparametric inference procedures for one, two and multiple populations.
  • Understand the basic analysis of categorical data (binomial, multinomial and bivariate).
  • Understand the uses of simple and multiple linear regression.

Assessment tasks

  • Assignments
  • Test One
  • Test Two
  • Exam