STATS221-19A (HAM)

Statistical Data Analysis

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)

Steven Miller

Steven Miller
6032
G.3.28
To be advised
steven.miller@waikato.ac.nz

Lecturer(s)

Paul Brown

Paul Brown
5279
G.3.30
To be advised
paul.brown@waikato.ac.nz

Lyn Hunt

Lyn Hunt
8338
G.3.21
To be advised
lyn.hunt@waikato.ac.nz

Steven Miller

Steven Miller
6032
G.3.28
To be advised
steven.miller@waikato.ac.nz

Administrator(s)

: rachael.foote@waikato.ac.nz

Placement Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: debby.dada@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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STATS221 extends the data collection and analysis techniques introduced in first year statistics courses. It is the gateway paper in the progression of frequentist statistics through to third year level. Students who pass this course will be prepared to cope with typical statistical problems in any field of research or practice.

The application of statistics is the primary focus of this course. Students will be taught to use the statistical software R to perform useful analyses, interpret the output and convey the results of such analyses. No prior experience with R is assumed.

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Paper Structure

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STATS221 is taught through weekly lectures. A computer lab in R-block will be reserved for use for STATS221 students to work on assignments throughout the semester.
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Analyse data using R.
    Linked to the following assessments:
  • Identify appropriate techniques to use when analysing data, within the scope of techniques covered.
    Linked to the following assessments:
  • Communicate the results of analyses by writing short reports.
    Linked to the following assessments:
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Assessment

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The coursework-to-final exam ratio will be 50:50. The pass mark is 50% overall. However you must achieve at least 40% in both coursework and the final exam to achieve an unrestricted pass.
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Assessment Components

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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. Assignment 1
22 Mar 2019
11:00 AM
10
  • Hand-in: Assignment Box (G Block)
2. Test 1
3 Apr 2019
10:00 AM
10
  • Hand-in: In Lecture
3. Assignment 2
3 May 2019
11:00 AM
10
  • Hand-in: Assignment Box (G Block)
4. Assignment 3
21 May 2019
11:00 AM
10
  • Hand-in: Assignment Box (G Block)
5. Test 2
29 May 2019
10:00 AM
10
  • Hand-in: In Lecture
6. Exam
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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Introduction to the Practice of Statistics 7th Edition (with CD containing chapters 16-17) David Moore, George McCabe and Bruce Craig (on course reserve in the Library).
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Other Resources

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Additional material is taken from

Linear Statistical Models - Bruce Bowerman and Richard O'Connell

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Online Support

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Copies of slides will be available after the lectures from the STATS221 page on Moodle (http://elearn.waikato.ac.nz). You can print any notes you require from the computer laboratory. Assignments will also be uploaded to Moodle. We will endeavor to record lectures using the Panopto system, which are then uploaded to Moodle. However, we offer no guarantee that the lectures will be recorded successfully.
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Workload

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We expect students to spend around 10 hours per week on this paper, including the 3 hours of lectures and 2 hours of work in the computer lab.
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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: At least one of STAT111, STAT121, STATS111, STATS121.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STAT221

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