37151 Introduction to Statistics
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2019 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Anti-requisite(s): 33116 Statistical Design and Analysis AND 33230 Mathematical Modelling 2 AND 33290 Statistics and Mathematics for Science AND 35151 Introduction to Statistics
Description
This subject focuses on data analysis. Students learn how to collect and analyse data, and how to draw valid conclusions from the data. The subject begins with a discussion of how to sample from a population and how to describe the data collected. This is followed by a discussion of how to form and test hypotheses about the population using the data collected from the sample.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | select and produce appropriate graphical, tabular and numerical summaries of variables in a data set, and summarise such information verbally; |
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| 2. | distinguish between observational and experimental studies, and draw conclusions appropriate to each type of study; |
| 3. | determine whether an interval or a test is more appropriate for addressing a particular question, and apply these concepts to answer questions using real data involving a single variable; |
| 4. | choose the appropriate type of inference to answer questions using real data involving several variables; |
| 5. | analyse, assess and critique statistical arguments of the type found in the popular press and in scholarly publications. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- An understanding of the nature, practice and application of the chosen science discipline. (1.0)
- Encompasses problem-solving, critical thinking and analysis attributes and an understanding of the scientific method of knowledge acquisition. (2.0)
- The ability to acquire, develop, employ and integrate a range of technical, practical and professional skills, in appropriate and ethical ways within a professional context, autonomously and collaboratively and across a range of disciplinary and professional areas, e.g. time management skills, personal organisation skills, teamwork skills, computing skills, laboratory skills, data handling, quantitative and graphical literacy skills. (3.0)
- The capacity to engage in reflection and learning beyond formal educational contexts that is based on the ability to make effective judgments about one's own work. The capacity to learn in and from new disciplines to enhance the application of scientific knowledge and skills in professional contexts. (4.0)
- An understanding of the different forms of communication - writing, reading, speaking, listening - including visual and graphical, within science and beyond and the ability to apply these appropriately and effectively for different audiences. (6.0)
Contribution to the development of graduate attributes
This subject provides students with the skills and understanding to apply appropriate statistical techniques and methods in solving problems in a variety of professional fields. It also helps students appreciate the need for critical and independent evaluation of statistical problems and the effective communication of the results of the statistical analysis. Thus this subject is contributing to the following graduate attributes:
Graduate Attribute 1 - Disciplinary knowledge and its appropriate application.
The lectures and laboratory classes and exercises communicate knowledge and skills, and demonstrate how to apply both the knowledge and the skills to a variety of problems.
Graduate Attribute 2 - An Inquiry-oriented approach.
The lectures discuss various ways to address a particular question, and students will develop skills in determining the correct approach themselves in the laboratory classes.
Graduate Attribute 3 - Professional skills and their appropriate application.
The ability to work effectively and responsibly in a group is emphasised in the groupwork components of the in-class assessments. The use of specialist statistical software to implement straight-forward analyses of problems is assessed in the weekly exercises.
Graduate Attribute 4 - Ability and motivation for continued intellectual development.
The ability to independently collect and critically evaluate information is assessed in the in-laboratory critiquing tasks, where students must review articles in the popular media and academic press that use statistical methods and assess the use of statistical methods in the article.
Graduate Attribute 6 - Communication skills.
Presentation of written and oral solutions to problems using appropriate professional language is emphasised in the in-class assessments.
Teaching and learning strategies
This subject consists of a combination of complementary in-class and self-study activities. Before class meetings, students will be expected to engage with background material that will introduce the fundamental concepts of statistics. The face-to-face classes (two 90 minutes of lecture/collaborative seminars and 120 minutes of computer laboratories) will incorporate a range of teaching and learning strategies including the presentation of worked examples, critiquing readings, collaborative group work and individual problem solving. Students are to review the relevant material made available on UTSOnline prior to classes, and complete any associated tasks, before attending the corresponding session. Students will use specialist statistical software extensively in the laboratory classes to analyse real data sets, both in groups and individually. Online Exercises are provided for which feedback on student attempts is given immediately.
Minor differences apply to the Teaching and Learning Strategies and Assessments when this course is offered in Block Mode. Please see Additional Information for details.
Content (topics)
The major topics covered in this subject are:
- Data collection - methods and limitations
- Data analysis
- Statistical estimation
- Critical thinking about data-based claims
Assessment
Assessment task 1: Weekly Online Exercises
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. disciplinary knowledge and its appropriate application 2. an inquiry-oriented approach 3. professional skills and their appropriate application |
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| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.0, 2.0 and 3.0 |
| Weight: | 20% |
| Criteria: | Students will be assessed on: the accuracy of their analysis. |
Assessment task 2: In-Laboratory Critiquing Tasks
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. disciplinary knowledge and its appropriate application 3. professional skills and their appropriate application 4. ability and motivation for continued intellectual development 6. communication skills |
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| Objective(s): | This assessment task addresses subject learning objective(s): 2, 3 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.0, 3.0, 4.0 and 6.0 |
| Weight: | 15% |
| Criteria: | Students will be assessed on:
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Assessment task 3: In-Laboratory Presentation Tasks
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. disciplinary knowledge and its appropriate application 3. professional skills and their appropriate application 6. communication skills |
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| Objective(s): | This assessment task addresses subject learning objective(s): 1, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.0, 3.0 and 6.0 |
| Weight: | 15% |
| Criteria: | Students will be assessed on:
|
Assessment task 4: Examination
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. disciplinary knowledge and its appropriate application 6. communication skills |
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| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.0 and 6.0 |
| Weight: | 50% |
| Criteria: | Students will be assessed on:
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Minimum requirements
Student must obtain at least 40% of the marks available for the final examination in order to pass this subject. If 40% is not reached, an X grade fail may be awarded for the subject, irrespective of an overall mark greater than 50.
Students should demonstrate competence in all aspects of the assessment in order to pass the subject. To pass the subject, a student must achieve a final result of 50% or more. The final result is simply the sum of all the marks gained in each piece of assessment.
Recommended texts
1. Statistics: The Art and Science of Learning from Data - 4th Edition by Alan Agresti, Christinr Franklin, Bernhard Klingenberg. Pearson 2017
This textbook can be purchased as an eBook direct from Pearson. See links in UTSOnline under Subject Orientation. Alternative suppliers will also have this text and may have a better price. The text comes with a suite of useful resources to assist students in learning the statistical concepts covered in this course.
2. Stats: Data and Models, Global Edition (4e) By Richard De Veaux, Paul Velleman, David E. Bock. Pearson 2016
E-Book: ISBN 9781292101644 - $AUD 60.00Stats: Data and Models
Paperback: ISBN 9781292101637 - $111.75 (for example, from Booktopia)
