STATS121-22A (NET)

Introduction to Statistical Methods

15 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Mathematics and Statistics

Staff

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Convenor(s)

Lecturer(s)

David Chan

David Chan
9068
G.3.09
See Moodle
david.chan@waikato.ac.nz

Administrator(s)

: maria.admiraal@waikato.ac.nz
: buddhika.subasinghe@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: alistair.lamb@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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STATS121-22A introduces statistical data collection and analysis for students in statistics, science and technology, computer science and the social sciences. It covers a selection of the statistical tools necessary for the effective use and analysis of data in research and practice. Topics covered include:

  • General principles for statistical problem-solving.
  • Sampling and experimental design.
  • Techniques for extracting information from data.
  • Some practical examples of statistical inference.
  • The study of relationships between variables using regression analysis.

Moodle is our primary avenue of communication to students, and students can find all of the necessary course material there.

R is an open-source statistical software package we use for most data visualisation and data analysis throughout STATS121-22A. Using R via an integrated work environment (IDE), such as RStudio, is recommended to make R more accessible for those not familiar with programming languages.

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Paper Structure

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Lectures
Lectures will be recorded and uploaded onto Moodle via Panopto. They provide the background, theoretical material, and general information of the paper. Each teaching week has about 3 hours of content via three 1-hour lectures. Students are expected to watch the lecture recordings at their earliest possible convenience.

Workshops
There are 1-hour workshop sessions held at the end of Weeks 1–5 and Weeks 7–11. The lecturer will consolidate the material from the lectures and typically involve live demonstrations of R (via RStudio).

Workshops are recorded for Unistart students. The lecturer aims to upload workshop material on Moodle on the day of the workshop.

Zoom Lab
There is a 1-hour Zoom lab held on Weeks 2–6 and Weeks 8–12. Students will find it beneficial to attend labs to complete their lab assignments. Unistart students are welcome to participate, though their appointed coordinator may conduct an in-person lab session in addition to the Zoom lab. In these labs, you will have an opportunity to perform and learn the more practical aspects of the course, particularly using R (via RStudio). Also, the lecturer is available to help you as needed in the scheduled Zoom lab.

COVID-19
If teaching cannot be conducted on campus due to COVID-19 restrictions, e.g. government or university regulations, we are prepared and ready to deliver this course entirely online. The lecturer will notify students of any changes through Moodle.


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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • Learning Outcomes
    • Collect and make effective use of data to answer questions within the scope of the techniques taught in this course. The lecture schedule specifies the range of techniques covered.
    • Identify which technique(s) to apply for a particular type of question.
    Linked to the following assessments:
  • Generic Skills

    In addition to the discipline-based learning objectives, the paper aims for students to develop their skills in:

    • Manipulating and analysing data using R (via RStudio).
    • Critical thinking and problem-solving skills.
    Linked to the following assessments:
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Assessment

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Internal assessment
This course is 100% internally assessed. The assessments comprise of weekly assignments (10 in total), two trimester tests, and a final test.

  1. One final test (50% total).
  2. Two open-book, online trimester tests (30% total).
  3. Ten weekly lab assignments (20% total).
    • The first lab assignment consists of two brief sub-assignments.

Final test
The final test will be a supervised test. The lecturer will give details to the students during the trimester. Note that the final test is compulsory, and those that do not complete this assessment will be given an IC grade.

Lab assignments
There are ten lab assignments throughout the trimester, each worth 10 marks, where 0 is the lowest possible mark and 10 is the highest possible mark. They contribute to 20% of your final mark. Additionally, the lecturer may assign a portion of those 10 marks as preparation marks for each lab. These will be assigned according to how much of the lab's preparation work you have done. Lastly, the first lab assignment's two brief sub-assignments are worth 5 marks each (such that the first lab is worth 10 marks in total).

The lecturer will post each lab assignment on Moodle a week before it is due (before Friday's lecture). The lab assignment should be completed and handed in for marking by 11:30pm the following Friday (see Schedule for the dates).The first lab assignment is an exception to this general due date, where the first sub-assignment is due on 11:30pm Tuesday 15th March and the second sub-assignment is due on 11:30pm Friday 18th March.

Lab assignments must be submitted online through Moodle either as a Microsoft Word document, an HTML document, or a PDF document. The last two options are for students who opt to use alternatives, such as R Markdown (via RStudio), to write their assignments. The lecturer will provide further instructions to students before labs start.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Test 1
13 Apr 2022
6:30 PM
15
  • Online: Submit through Moodle
2. Test 2
8 Jun 2022
6:30 PM
15
  • Online: Submit through Moodle
3. Lab Assignments
20
  • Online: Submit through Moodle
4. Final Test
50
✔
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Recommended Readings

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Mind on Statistics (5th edition) by Utts and Heckard.

Those students with copies of the 2nd, 3rd or 4th Edition of Mind on Statistics by Utts and Heckard, should find it adequate as a reference book for the topics covered.

Copies of these books are available from the University of Waikato library.

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Other Resources

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Course Materials

Lecture slides, workshop material, and any additional content will be made available on Moodle: elearn.waikato.ac.nz

All other course material, such as lab assignments and the suggested lab assignment answers, can be accessed via Moodle.

Lecture Recordings

Lectures are recorded via Panopto, and a link to each recording will be accessible through the Moodle page. The lecturer may add additional Panopto material from time to time.

R

R is an open-source statistical software package. It is available to download for free at the following link: r-project.org

RStudio

RStudio is an integrated development environment for R. The RStudio Desktop version of the environment is available to download for free at the following link: rstudio.com/products/rstudio/download/

Microsoft Word

Microsoft Word is part of Microsoft Office, and Office is available for free for all University of Waikato students. Instructions and details for downloading this software is available at the following link: waikato.ac.nz/ict-self-help/guides/free-microsoft-office-suite-download

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Online Support

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Lectures will be recorded and uploaded onto Moodle via Panopto.

A Zoom lab session is available for those taking this paper.

Office Hours are also available for those taking this paper (see Moodle for details).

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Workload

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Students should expect to spend a minimum of about 10 hours per week on this paper. This includes the 4–5 contact hours mentioned in Paper Structure and Timetable each week (lectures, workshop, and lab).
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Linkages to Other Papers

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Prerequisite(s)

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STATS111, DATAX111, DATAX121

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