最新糖心Vlog

PUB HLTH 7074OL - Introduction to Biostatistics

Online - Semester 1 - 2015

Biostatistics is the application of statistical methods (summarising data and drawing valid inferences based on limited information) to biological systems, more particularly, to humans and their health problems. This course deals with statistical concepts and terminology and basic analytic techniques. The purpose of the course is to give public health and occupational health workers an introduction to the discipline, an appreciation of a statistical perspective on information arising from the health arena and basic critical appraisal skills to enable graduates to assess the quality of research evidence.

  • General Course Information
    Course Details
    Course Code PUB HLTH 7074OL
    Course Introduction to Biostatistics
    Coordinating Unit Public Health
    Term Semester 1
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Online
    Available for Study Abroad and Exchange N
    Incompatible PUB HLTH 7074
    Course Staff
    Course Coordinator: Dr Amy Salter
    Phone: +61 8313 4619
    Email: amy.salter@adelaide.edu.au
    Location: Level 8 Hughes Building

    Course Coordinator: Dr Lynne Giles
    Phone: +61 8313 0234
    Email: lynne.giles@adelaide.edu.au
    Location: Level 7, 178 North Terrace

    Learning and Teaching Team
    Phone: +61 8313 2128
    Email: postgrad_enq@adelaide.edu.au
    Location: Level 7, 178 North Terrace
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will be able to:

    1 Apply basic statistical concepts commonly used in Health Sciences;
    2 Use basic analytical techniques to generate results;
    3 Interpret results of commonly used statistical analyses in written summaries; and
    4 Demonstrate statistical reasoning skills correctly and contextually.
    最新糖心Vlog Graduate Attributes

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

    最新糖心Vlog Graduate Attribute Course Learning Outcome(s)
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1,2,3
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2,3,4
  • Learning Resources
    Required Resources

    The textbook for this course is: Armitage P, Berry G, Matthews JNS. Statistical Methods in

    Medical Research (4th edition). 2002 (2008 for electronic edition); Wiley-Blackwell, London. 

    Course Handbook and Study Guides will be made available to students before Week 1 of the semester and will be available in electronic form on MyUni.
     
    Please note: an electronic version of the textbook may be accessed for free via the Barr Smith Library. It is accessed via the ‘ebrary’ portal which will be discussed in Week 1 on Blackboard in MyUni. Supplementary reading material may also be placed on MyUni throughout the course, as required.
    Recommended Resources
    N/A
    Online Learning
    MyUni will provide the online learning system for students via
    Once students have sucessfully enrolled in this course they can access the MyUni site where they can access lectures, tutorials, assignments, join discussion forums and link up with the course co-ordinators and fellow students.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    There are a number of teaching and learning modes in this course. The online course lectures provide basic factual information and concepts in biostatistics. Lectures are intended to supplement material covered in the study guides and readings. Lectures will be supported by online tutorials with directed learning to text, videos, and websites. The tutorials are designed to develop and clarify topics covered in the readings and lectures. These are generally problem solving sessions and students are required to complete as many questions as possible prior to the Blackboard discussion date. Assignments provide an opportunity to undertake exploratory and in-depth analysis of some key concepts introduced in the course. Finally, the exam will assess the extent to which students have developed their biostatistical understanding through the course.
    Workload

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

    Consistent of 3 assignments plus an examination and weekly readings.
    Learning Activities Summary

    Week Topic Lecture/Tutorial
    Week 1 Introduction to Biostatistics and Descriptive Statistics Lecture: Introduction to Biostatistics and Descriptive Statistics
    Tutorial: Administration of diagnostic tool and Descriptive Statistics
    Week 2 Probability and probability distributions 1 Lecture: Probability concepts, Laws of probability
    Tutorial: Further descriptive statistics
    Week 3 Probability and probability distributions 2 Lecture: Probability distributions and sampling distributions
    Tutorial: Probability and probability distributions
    Week 4 Inferential Statistics 1 Lecture: Null and alternative hypotheses and how to set up a statistical test
    Tutorial: The binomial probability distribution and the Normal distribution
    Week 5 Inferential Statistics 2 Lecture: Sample statistics and population parameters, confidence intervals
    Tutorials: Setting up a statistical test, errors and power
    Week 6 Comparison between two independent groups Lecture: Conducting a z-test, the t-distribution, conducting a t-test for independent samples
    Tutorial: Calculation of a confidence interval
    Week 7 Comparison between two matched or paired groups Lecture: Examples of matching and pairing t-test for dependent samples
    Tutorial: Inference for independent samples
    Week 8 Categorical Data Lecture: An introduction to the chi-square test of association
    Tutorial: Inference for paired samples
    Week 9 Simple linear regression 1 Lecture: Method of least squares, definition of residuals
    Tutorial: Calculating a chi-square test of association
    Week 10 Simple linear regression 2 Lecture: Assumptions of simple linear regression model, assessing assumptions
    Tutorial: Simple linear regression
    Week 11 Correlation Lecture: Pearson's correlation coefficient, inference and interpretation of correlation coefficients
    Tutorial: More on simple linear regression and assumptions
    Week 12 Course overview and revision
    Week 13 Self directed study for exam revision
    Specific Course Requirements
    N/A
    Small Group Discovery Experience
    N/A
  • Assessment

    The 最新糖心Vlog'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
    Assessment Task Task Type Weighting Learning course objective(s) being addressed
    Assignment 1: Summary statistics Summative 5% 1, 3, 4
    Assignment 2: Calculating probabilities and defining hypotheses Summative 15% 1, 3, 4
    Assignment 3: Inferential statistics Summative 20% 1-4
    Examination Summative 60% 1-4
    Assessment Related Requirements
    N/A
    Assessment Detail
    There will be three assignments and an examination in this course.  All assignments will be posted on MyUni at least 2 weeks prior  to the date of submission.  Students will be required to participate in an on-line, open book examination for a period of two hours  that will be scheduled in synchrony for all students.

    Assignment 1: Descriptive statistics (5%)
    The first assignment will assess students’ facility with calculating appropriate descriptive summary statistics of samples of data.  Due at end of Week 3 of Semester 1.

    Assignment 2: Probability and hypotheses (15%)
    The second assignment will assess students’ facility with using simple probability distributions and formulating null and alternative hypotheses. 
    Due at end of Week 6 of Semester 1.

    Assignment 3: Inferential statistics (20%)
    In this assignment, students will select and calculate appropriate test statistics based on scenarios drawn from the population health literature. 
    Due at end of Week 12 of Semester 1.

    Examination (60%)
    The students will be required to demonstrate their statistical reasoning skills by way of calculating descriptive statistics, formulating  null and alternative hypotheses, performing inferential statistical tests, and interpreting the results of their calculations.  Scheduled in  Semester 1 examination period.
    Submission
    Information will be provided on MyUni
    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 .

  • Student Feedback

    The 最新糖心Vlog 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 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) 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

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.