最新糖心Vlog

Lecturer-in-charge/tutor: Lucy Andrew
Email: lucy.andrew@adelaide.edu.au 

Upon the successful completion of this course, students should be able to:

1. Discuss and apply basic concepts which are essential in statistics, including variance, probability, significance, and others;
2. Apply statistical knowledge to academic and everyday life;
3. Work cooperatively with others;
4. Analyse a specific dataset in response to a question in order to form well-supported conclusions;
5. Utilise technology to assist in the analysis and application of statistical knowledge.

Students will need to sign up to Khan Academy to receive homework questions. You can sign up using a Google or
Facebook account, or by using an email address. Students will be given the opportunity to sign up to Khan Academy during the first
tutorial

The lecturer will establish the recommended resources during weeks 1 and 2.

MyUni and Khan Academy (see above) will be essential portals for your learning in this course. It is very important that you are familiar with both these environments and use them effectively to support your learning.

Important information may be emailed to your student email account, so it is essential that you check your student email regularly.

This course will entail 1 hour of lectures per week and 2 hours of workshops per week. Students are expected to discuss key issues and concepts presented in the course. Most importantly, students are expected to relate the concepts discussed in the lecture/workshop to everyday life situations and applications. The student will be responsible for the non-contact activities which will include, but are not limited to, reading and studying.

1 hour lecture per week (x12)	12 hours
2 hour tutorial per week (x12)	24 hours
4 hours online research per week (x12)	48 hours
6 hours reading and independent study per week (x12)	72 hours
Total	156 hours

Schedule

	Topic	Notes
Week 1	Introduction: Why use statistics? Descriptive vs. Inferential	5-6pm tutorial in Computer Lab
Week 2	Central tendency, range, frequency, distribution
Week 3	Variation; box and whisker plots	Test: descriptive v inferential stats, central tendency, range, max, min, frequency
Week 4	Graphical representations of data
Week 5	Distribution of data	Present data to the class. 5 minutes per group.
Week 6	No lecture, no tutorial	Data collection
	Mid-semester break	Data collection
Week 7	Gathering Data I
Week 8	Gathering Data II	Test: variation, interpreting graphs, distribution of data, box and whisker plots
Week 9	Data and Chance I
Week 10	Data and Chance II
Week 11	Exploring Data I	Test: Data and Chance (probability)
Week 12	Exploring Data II
Week 13	Exploring Data III	Assignment questions will be answered in the tutorial

For clarification on which dates correspond to which weeks, please visit:

To pass this course, students must attend at least 75% of tutorials; in cases of absence for medical or compassionate reasons, documentation must be provided and students must still attend at least 50% of
classes. If students fail to attend the minimum required number of tutorials, they will be considered to have not completed an assignment (see below).

The 最新糖心Vlog of Adelaide has committed to a pedagogical approach termed the “Small Group Discovery Experience”, indicating that the SHDE will be a core component in a credit-bearing course of every undergraduate program, and that it will be part of every first-year level from 2014. Since the UPP is not an award-based program, it is not strictly required to include an SGDE in the UPP.

However, since the UPP is designed to prepare students for first-year study, and the SGDE will be a core component of all first-year study, it is important for the UPP to provide some preparation in Small Group Discovery. These should be of a scaffolded, preparatory nature as befits each course within the program, and the philosophy and program objectives of the UPP. The Program has been designed to include  preparation for small group work and research activity in many of its courses.

More specifically, this course aims to prepare students for their small-group discovery experience by providing subject expertise in statistics and giving students the opportunity to develop an inquiry-based project to gather and analyse some data in order to draw conclusions.

Assessment Task	Task Type	Due	Weighting	Learning Outcome
Attendance and Participation	Formative	Ongoing	5%	1, 2, 3, 5
In-class mini-quizzes x3	Formative	In class (tutorial), Week 3, Week 8, Week 11	30% (3 x 10% each)	1, 4
Group presentations	Formative	In class (tutorial), Week 5	5%	1, 2, 3
Graphing Assignment	Summative	Friday, first week of mid-semester break	25%	2, 4, 5
Data Assignment	Summative	5pm Friday, Week 13	35%	1, 2, 4, 5

For clarification on which dates correspond to which weeks, please visit:  

Students must attempt all assessment tasks to pass this course. If students miss a mini-quiz they will receive a mark of zero, unless they receive an extension on medical or compassionate grounds as per the 最新糖心Vlog's Modified Arrangment of Coursework Assessment (MACA) policy.

Since the 最新糖心Vlog Preparatory Program is designed to prepare students for success at 最新糖心Vlog, completing and submitting all assignments is central to the intended learning outcomes of the program and each course within it. Often, at least attempting and submitting assignments in the face of difficulty or adversity is enough for success at 最新糖心Vlog and the UPP encourages this resilience by employing this policy in select courses. Please note that the absolute last date for the submission of assignments in Semester 1 is the end of Swot Vac week, which is one week after the final assignment is due.

If a student fails to submit all assessment tasks and would otherwise have received a grade greater than 45, they will be given a nominal grade of 45 (Fail) for that course in that semester. This will permit them to undertake additional assessment (formerly called academic supplementary assessment) at the Course Coordinator’s discretion, as per policy at http://www.adelaide.edu.au/student/exams/supps.html

It is not necessary to apply for additional assessment; this assessment will usually consist of the missed pieces of assessment, but the course coordinator may require more. As per policy 9.1.3.3, if the student
passes the additional assessment to the Course Coordinator’s satisfaction, the maximum grade they can get for the course is 50 (Pass). If a student’s raw grade is below 45, regardless of whether all tasks have been attempted, this score will stand unless exceptional, documented circumstances apply as per the 最新糖心Vlog’s Modified Arrangements for Coursework Assessment: /student/exams/mod_arrange.html 

Substantial non-engagement in this course (evidenced by repeated non-attendance at tutorials and failure to submit assessments) may result in students being withdrawn from the 最新糖心Vlog Preparatory Program and being required to apply for reinstatement if they wish to continue.

Attendance and Participation

Your lecturer/tutor will mark you on your participation in group and class discussions during tutorials. This mark is not based on 'correct' answers but a willingness to participate. Remember that being vocal in class is not the only way to demonstrate participation: asking questions after class or via email/MyUni, doing pre-readings, active listening and note-taking, and actively discussing with other students are all ways of showing that you are participating in the class.

Mini Quizzes

Three Mini Quizzes (MQs) will be conducted at the beginning of the tutorial in weeks 3, 8 and 11.

The MQ will consist of 3-5 short questions; either a specific piece of information (SPI question type), a selection from multiple choices (MC question type), an extended piece of information, or open-ended question (OE question type). MQ questions will be similar to those questions/exercises worked out during the lecture/workshop sessions and will be based on topics discussed in previous lectures/workshops.

SPI example: Find the mean of the following values: 5, 0, 7, 3, 6, and 3

MC example: Which of the following answers is the mean of the following values: 5, 0, 7, 3, 6, and 3?
A) 4
B) 7
C) 2.52
D) 0

OE example: What does the mean of a data set tell us about the data?

Group Presentation

Choose a data set (or your tutor will give you a data set). Use statistics to describe your data with specific reference ot the concepts and terms introduced in class. Graph your data (you may need to create more than one graph) and explain the meaning of the data to the class. What kinds of interpretations might your draw from the data? How might the data be used as evidence to support some kind of academic argument?

Graphing Assignment (500 words)

Compare and contrast 3 different graphical techniques for representing data. Use one data set and graph the data 3 different ways. What are the advantages/disadvantages of each type of graph? Which is the best graph to represent this data? Why?

The assignment must contain the following sections;

Introduction: Describe the data in terms of central tendency and variation. What is the population or sample? How was the data collected?
Body: Produce 3 types of graphs and discuss the advantages and disadvantages of each graph.
Conclusion: What can you conclude from the data? Which graph represents the data in the best way?

Data Assignment (700 words)

Choose a data set (or your tutor will give you a data set). What is the question you are trying to answer with your data? i.e. Are house prices in SA increasing at the same rate as house prices in NSW and Tasmania? How have 最新糖心Vlogns’ attitudes to gay marriage changed over the past 20 years? How does smoking effect the risk of heart attack in people over 50? Try to present your data in a way that support a sepcific conclusion/answer to your question. In this assignment, you should:

• Briefly describe how the data was collected
• Use statistics to describe your data (central tendency and variation)
• Graph the data in a way that best represents the data
• Interpret the data – what it the data telling you? You may want to calculate percentages, probability, frequency etc. You may need to include extra data to help interpret your original data set.
• Make conclusions – What can you conclude from your data? What can you NOT conclude i.e. identify a possible misinterpretation of the data by someone who doesn’t know as much as you about statistics. Clearly state what extra data you need to make relevant conclusions.

All assignments will be electronically submitted via MyUni, except for tests and group presentations.

Students may be granted extensions to assignments on medical or compassionate grounds; documentation to support these ground will be required. Requests for extension must be made before the due date; requests for extension submitted after the due date will not be considered. All extension requests must be submitted to the Course Coordinator (Chad Habel: chad.habel@adelaide.edu.au);
any extensions granted by the lecturer or tutor will not be considered valid.

All extension requests will be administered according to the Modified Arrangements for Coursework Assessment Policy:

For a concise information sheet on this policy, please visit
 
Penalties for Late Submission

Unless the Course Profile states otherwise when an assessment is submitted after the due date, and without an extension, 5% of the total mark possible will be deducted for every 24 hours or part thereof that it is late, including each day on a weekend. For example, an essay that is submitted after the due date and time but within the first 24 hour period, and that has been graded at 63%, will have 5% deducted, for a final grade of 58%. An essay that is more than 24 hours late will lose 10%, etc. 

This course aims to return assessed work within 2 weeks of its submission, although this cannot be guaranteed. The resubmission of assignments is not possible for this course, except in exceptional circumstances as approved by the Course Coordinator.