STATS121-22A (HAM)

Introduction to Statistical Methods

15 Points

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

Staff

Edit Staff Content

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.
Edit Staff Content

Paper Description

Edit Paper Description Content

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.

Edit Paper Description Content

Paper Structure

Edit Paper Structure Content

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 either attend the lectures OR watch the lecture recordings at their earliest possible convenience.

Workshops
There are 1-hour face-to-face workshop sessions held on Weeks 1–5 and Weeks 7–11 during the second hour of the Friday lecture. The lecturer will consolidate the material from the lectures and typically involve live demonstrations of R (via RStudio). Additionally, one-on-one help directly from the lecturer is available if time permits (in addition to Office Hours).

Workshops are voluntary and may not be recorded. The lecturer aims to upload workshop material on Moodle on the day of the workshop.

Labs
There are 1-hour face-to-face labs held on Weeks 2–6 and Weeks 8–12 in the R.G.12 computer laboratory. Students will find it beneficial to attend labs to complete their lab assignments (see Labs under Timetable for more details). 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, statistics demonstrators are available to help you as needed in the scheduled lab times.

Zoom labs are available for those taking this paper off-campus (see Labs under Timetable for details).

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.

Edit Paper Structure Content

Learning Outcomes

Edit Learning Outcomes Content

Students who successfully complete the course 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:
Edit Learning Outcomes Content
Edit Learning Outcomes Content

Assessment

Edit Assessments Content

Final assessment
The assessment is comprised of two components: your internal assessment and your final exam. The internal assessment and final exam ratio is 50:50. However, you must achieve at least 40% in both internal assessment and the final exam to achieve a clear pass.

Internal assessment

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

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 tutorials start.

Edit Additional Assessment Information Content

Assessment Components

Edit Assessments Content

The internal assessment/exam ratio (as stated in the University Calendar) is 50:50. There is no final exam. The final exam makes up 50% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 50:50 or 0:0, whichever is more favourable for the student. The final exam makes up either 50% 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. Exam
50
✔
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
Edit Assessments Content

Required and Recommended Readings

Edit Required Readings Content

Recommended Readings

Edit Recommended Readings Content

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.

Edit Recommended Readings Content

Other Resources

Edit Other Resources Content

Course Materials

There is no longer a physical coursebook for STATS121. 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 Block Computer Labs

The R Block Computer Labs are available to enrolled students Monday to Friday, 8am to 9pm (see cms.waikato.ac.nz/student-resources/computer-support/labs). Like R.G.12, the other computer labs also have bookings throughout the day. If another paper has booked the lab and a computer is available, you may be able to use the lab, but this is at the discretion of the tutor(s) in charge.

A Cardax Swipe card is required to access the computer labs at all other times (before 8am and after 9pm, and during the weekends). Please see the Mathematics & Statistics Reception (G.1.21) for details on obtaining a swipe card. Printing is available from the labs but will incur a charge.

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

Edit Other Resources Content

Online Support

Edit Online Support Content

Lectures will be recorded and uploaded onto Moodle via Panopto (see Lectures under Timetable for details).

Zoom lab(s) are available for those taking this paper off-campus (see Labs under Timetable for details).

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

Edit Online Support Content

Workload

Edit Workload Content
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).
Edit Workload Content

Linkages to Other Papers

Edit Linkages Content

Prerequisite(s)

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STATS111, DATAX111, DATAX121

Edit Linkages Content