STATS 1004 - Statistical Practice I (Life Sciences)
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code STATS 1004 Course Statistical Practice I (Life Sciences) Coordinating Unit School of Mathematical Sciences Term Semester 1 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 1000, STATS 1005, ECON 1008, STATS 1504 Assumed Knowledge SACE stage 2 Mathematical Methods Restrictions Not available to BMaSc or BMaSc (Adv) students Course Description 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 science, technology, government, commerce, manufacturing and the life sciences. 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 range of real world situations. It has a strong practical focus using the statistical package R to analyse real data relevant to the life sciences.
Topics covered are: organisation, description and presentation of data in the life sciences; 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.
Course Coordinator: Dr Stephen Crotty
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
- Apply methods for scientific problem-solving.
- Demonstrate an ability to plan simple experiments and surveys.
- Recognise the appropriate techniques for the analysis of a variety of experimental and observational studies.
- Appreciate statistics as a coherent discipline in its own right.
- Demonstrate a sound preparation for a more theoretical and mathematical study of statistics at Levels II and III.
- Use a modern statistical computing package.
- 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)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Required ResourcesMoore, McCabe, and Craig - Introduction to the Practice of Statistics (8th Ed).
Online LearningThis course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that students make appropriate use of these resources.
Link to MyUni login page:
Learning & Teaching Activities
Learning & Teaching ModesThis 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 12 22 Assignments 5 48 Practicals 12 14 Total 156
Learning Activities SummaryTopic Outline
1. Looking at a single variable
2. Relationships between variables
3. Producing data
5. Probability distributions
6. Measurement data
7. Analysis of variance
8. Simple linear regression
9. Count data
1. Descriptive statistics
2. Relationships between variables
4. Producing data
6. Probability distributions
7. One sample inference
8. Hypothesis testing
9. Analysis of variance
10. Inference for regression
11. Inference for count data
1. Introduction to R
2. Descriptive statistics and graphs
3. Relationships between variables
4. Predictor response relationships
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
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Component Weighting Outcomes Assessed Online quizzes 5% All Mid Semester Major Quiz 20% All Assignment 1 5% All Assignment 2 5% All Assignment 3 5% All Assignment 4 5% All Assignment 5 5% All Exam 50% All
Assessment Item Distributed Due Date Weighting Assignment 1 Week 1 Week 2 5% Assignment 2 Week 2 Week 4 5% Assignment 3 Week 4 Week 6 5% Assignment 4 Week 6 Week 8 5% Assignment 5 Week 8 Week 10 5% Online quizzes End of each week Week 13 5% (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.
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.
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 (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.
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