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STATS 1000 - Statistical Practice I
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 3 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: Louise Campbell
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
Learning Outcomes
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. |
1,2,3,5,6,7 |
|
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. |
1,2,3,5,6,7 |
Learning Resources
Required Resources
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:
https://myuni.adelaide.edu.au/webapps/login/
Learning & Teaching Modes
Workload
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 & Teaching Activities
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
Worksheets
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:
- 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.
Assessment Summary
| Component | Weighting | Objective 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 Related Requirements
Furthermore, students must achieve at least 40% on the final examination 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 | 5% |
| Online quizzes | End of each week | Week 13 | 5% (total) |
Submission
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 |