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Unit outline_

STAT5002: Introduction to Statistics

Semester 2, 2023 [Normal evening] - Camperdown/Darlington, Sydney

The aim of the unit is to introduce students to basic statistical concepts and methods for further studies. Particular attention will be paid to the development of methodologies related to statistical data analysis and Data Mining. A number of useful statistical models will be discussed and computer oriented estimation procedures will be developed. Smoothing and nonparametric concepts for the analysis of large data sets will also be discussed. Students will be exposed to the R computing language to handle all relevant computational aspects in the course.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

HSC Mathematics

Available to study abroad and exchange students

No

Teaching staff

Coordinator Yves Tam, yves.tam@sydney.edu.au
Lecturer(s) Yves Tam, yves.tam@sydney.edu.au
Tutor(s) Rachel Wang, rachel.wang@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Extended answer questions (EAQ)
60% Formal exam period 2 hours
Tutorial quiz Module assignment quizzes
4%
4% Multiple weeks
Due date: 26 Aug 2023 at 23:59
Online Quiz 1
Supervised test
? 
Mid-semester quiz
Summative
20% Week 08
Due date: 23 Sep 2023 at 11:00

Closing date: 23 Sep 2023
1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Tutorial quiz Online Quiz #2
4%
4% Week 08
Due date: 23 Sep 2023 at 23:59

Closing date: 23 Sep 2023
Online Quiz 2
Tutorial quiz Online Quiz #3
4%
4% Week 12
Due date: 28 Oct 2023 at 23:59

Closing date: 28 Oct 2023
Online Quiz 3
Assignment Assignment
Individual
8% Week 13
Due date: 03 Nov 2023 at 23:59

Closing date: 03 Nov 2023
One week duration. Online submission.
Outcomes assessed: LO1 LO2 LO8 LO9 LO10 LO11 LO12 LO13 LO14 LO15 LO16 LO17 LO18

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Representing complete or close to complete mastery of the material.

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery.

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Graphical & numerical summaries Lecture and tutorial (3 hr)  
Week 02 Probability Lecture and tutorial (3 hr)  
Week 03 Random variables Lecture and tutorial (3 hr)  
Week 04 Testing for a mean Lecture and tutorial (3 hr)  
Week 05 Testing for proportions Lecture and tutorial (3 hr)  
Week 06 Goodness of fit tests Lecture and tutorial (3 hr)  
Week 07 Confidence intervals Lecture and tutorial (3 hr)  
Week 08 Bivariate data Lecture and tutorial (3 hr)  
Week 09 Multiple linear regression Lecture and tutorial (3 hr)  
Week 10 Model selection Lecture and tutorial (3 hr)  
Week 11 Logistic regression and non-parametric regression Lecture and tutorial (3 hr)  
Week 12 Bayesian inference Lecture and tutorial (3 hr)  
Week 13 Bayesian inference, Revision Lecture and tutorial (3 hr)  

Attendance and class requirements

Students are expected to attend the weekly lectures and assigned tutorials.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

 The following texts are recommended:

  1. All of Statistics, Larry Wasserman, Springer (2004)
  2. David Freedman, Robert Pisani and Roger Purves.  Statistics.  Norton, 2007.
  3. R Development Core Team:  An Introduction to R.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. use the R statistical computing environment to obtain numerical and graphical summaries of data, and for performing various statistical calculations
  • LO2. explain univariate and bivariate data by means of the five number summary, mean, variance and standard deviation, correlation coefficient, boxplot, histogram and scatterplot
  • LO3. use methods derived from the three axioms of probability to calculate the probabilities of simple events
  • LO4. understand the concept of a random variable and the meaning of the expected value and variance
  • LO5. apply the Binomial distribution as a model for discrete data
  • LO6. use the Normal distribution as a model for continuous data
  • LO7. understand the central limit theorem
  • LO8. understand the concept of hypotheses tests and p-values for finding evidence for or against simple null hypotheses, in particular using the binomial test for testing proportions, one- or two-sided z-, t- or sign-test for making inference about the population mean
  • LO9. use the Chi-squared test for simple goodness of fit problems
  • LO10. understand the concept of a confidence interval
  • LO11. understand the fundamental difference between frequentist based inference and bayesian inference
  • LO12. find the least squares regression line as a way of describing a linear relationship in bivariate data
  • LO13. use R to analyse multivariate data
  • LO14. use of the general F-test as the main tool to choose between two nested regression models
  • LO15. assess model assumptions and outlier detection in regression models through standard diagnostic plots (box plot, scatterplot, Q-Q-plot, Cook’s distance plot, leverage vs residual plot), through influence measures (leverage values, Cook’s distance)
  • LO16. apply multiple linear regression and in the understanding of R2 and the adjusted R2
  • LO17. calculate and interpret confidence intervals for all parameters in linear regression
  • LO18. use model selection through using the F-test, t-test, AIC or BIC through full searches or by using step-wise procedures (backward, forward, stepwise)
  • LO19. apply polynomial regression models
  • LO20. apply logistic and non-parametric regression.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
LO1         
LO2         
LO3         
LO4         
LO5         
LO6         
LO7         
LO8         
LO9         
LO10         
LO11         
LO12         
LO13         
LO14         
LO15         
LO16         
LO17         
LO18         
LO19         
LO20         

This section outlines changes made to this unit following staff and student reviews.

Added these books for additional reading and R.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

 

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances
  • A laboratory coat and closed-toe shoes are mandatory
  • Follow safety instructions in your manual and posted in laboratories
  • In case of fire, follow instructions posted outside the laboratory door
  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.