STATS 1000 - Statistical Practice I

North Terrace Campus - Semester 2 - 2021

Statistical ideas and methods are essential tools in virtually all areas that rely on data to make decisions and reach conclusions. This includes diverse fields such as medicine, science, technology, government, commerce and manufacturing. In broad terms, statistics is about getting information from data. This includes both the important question of how to obtain suitable data for a given purpose and also how best to extract the information, often in the presence of random variability. This course provides an introduction to the contemporary application of statistics to a wide range of real world situations. It has a strong practical focus using the statistical package R to analyse real data. Topics covered are: organisation, description and presentation of data; design of experiments and surveys; random variables, probability distributions, the binomial distribution and the normal distribution; statistical inference, tests of significance, confidence intervals; inference for means and proportions, one-sample tests, two independent samples, paired data, t-tests, contingency tables; analysis of variance; linear regression, least squares estimation, residuals and transformations, inference for regression coefficients, prediction.

  • General Course Information
    Course Details
    Course Code STATS 1000
    Course Statistical Practice I
    Coordinating Unit Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange Y
    Incompatible MATHS 2107, STATS 1004, STATS 1005, ECON 1008, STATS 1504
    Assumed Knowledge SACE stage 2 Mathematical Methods
    Restrictions Not available to BMaSc or BMaSc (Adv) students
    Assessment Ongoing assessment, exam
    Course Staff

    Course Coordinator: Dr Shenal Dedduwakumara

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    1. Apply methods for scientific problem-solving.
    2. Demonstrate an ability to plan simple experiments and surveys.
    3. Recognise the appropriate techniques for the analysis of a variety of experimental and observational studies.
    4. Appreciate statistics as a coherent discipline in its own right.
    5. Demonstrate a sound preparation for a more theoretical and mathematical study of statistics at Levels II and III.
    6. Use a modern statistical computing package.
    7. Demonstrate a suitable grounding in statistics for those who are continuing in other fields and who may need to use statistics in later experimental studies.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
  • Learning Resources
    Required Resources
    Moore, McCabe, and Craig - Introduction to the Practice of Statistics (8th Ed).
    Online Learning
    This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that the students make appropriate use of these resources.

    Link to MyUni login page: 
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides the assessment opportunities for students to gauge their progress and understanding.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity Workload hours
    Lectures 36 72
    Tutorials 11 22
    Assignments 5 48
    Practicals 12 14
    TOTALS 156
    Learning Activities Summary
    Topic outline

    1. Looking at a single variable 
    2. Relationships between variables 
    3. Producing data 
    4. Probability 
    5. Probability distributions 
    6. Measurement data 
    7. Analysis of variance 
    8. Simple linear regression 
    9. Count data 


    1. Descriptive statistics
    2. Relationships between variables
    3. Regression
    4. Producing data
    5. Probability
    6. Probability distributions
    7. One sample inference
    8. Hypothesis testing
    9. Analysis of variance
    10. Inference for regression
    11. Inference for count data

    Practical Outline

    1. Introduction to R
    2. Descriptive statistics and graphs
    3. Relationships between variables
    4. Predictor response relationships
    5. Transformations
    6. Probability Calculations
    7. Normal Q-Q plots, one sample inference
    8. Two sample hypothesis tests and confidence intervals
    9. Analysis of Variance
    10. Inference for regression
    11. Analysis of count data
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Component Weighting Objective Assessed
    Online Quizzes 15% All
    Mid Semester Major Quiz 25% All
    Assignment 1 5% All
    Assignment 2 5% All
    Assignment 3 5% All
    Assignment 4 5% All
    Assignment 5 15% All
    Exam 25% All
    Assessment Related Requirements
    An aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    Assessment Item Distributed Due Date Weighting
    Assignment 1 Week 1 Week 2 5%
    Assignment 2 Week 3 Week 4 5%
    Assignment 3 Week 6 Week 6 5%
    Assignment 4 Week 6 Week 8 5%
    Assignment 5 Week 8 Week 10 15%
    Online quizzes End of each week Week 13 15% (total)

    All written assignments are to be submitted to the designated hand-in boxes in the School of Mathematical Sciences with a signed cover sheet attached. Late assignments will not be accepted. Assignments will have a two week turn-around time for feedback to students.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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