15 Points This paper is designed to provide an introduction to 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. It is a practical course that uses relevant examples to illustrate the
use of statistical methods.This paper is an essential paper for anyone planning to do research
or experiments in their studies or future careers.
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; and the study of relationships between variables using
regression analysis. The statistical computer software package, Minitab, is used for most of
the statistical computations and graphical displays. A Semester
Carolyn Munro G.2.28, extension 5170.
Official Timetable Information
Students who successfully complete this course should:
* have developed useful skills in collecting and making effective use of data for problems within the scope of the techniques we cover. The lecture schedule specifies the range of the techniques covered.
Generic Skills
In addition to the siscipline-based learning objectives, the paper aims for students to develop their skills in:
* manipulating and analysing data using Minitab;
* enhancing their critical reasoning. MATH168 Preparatory Mathematics or
18 credits at Level 2 NCEA Mathematics, or
14 credits at Level 3 NCEA Statistics, Calculus or Mathematics
STAT111 Statistics for Science
STAT160 Management Statistics
Lectures:
Lectures begin in the week commencing 3 March 2014. There are three one-hour lectures per week. Students should attend all lectures as they provide the background, the theoretical material and general information for the paper. The course notes booklet should be brought to lectures each week as it contains notes for the material covered. Extra material may also be covered in lectures.
Workshop:
Students should attend the one-hour workshop session each week. Exercises are set for each session. These exercises enable you to put the material covered in lectures into practice. Students should work through the set exercises.
Tutorials:
Students should attend one one-hour tutorial each week. Tutorials begin in the week commencing 10 March 2014 and are based on work covered in lectures. Students should sign up for their preferred tutorial time through Moodle. During these sessions you will use the statistical package Minitab and the techniques learnt during lectures to help solve statistical problems. You will also use Microsoft Word. Tutorial work will be assessed each session. Assessment details are listed below. Class attendance is expected. The lecture material and tutorials are all integral parts of the paper. Failure to attend any of these means the student may miss material not presented elsewhere. Students are responsible for all material covered in the paper.
Contact Hours: Three lectures, one workshop, and one tutorial each week.
This is comprised of two components, your internal coursework and your final exam. The internal coursework : final exam ratio will be (1/2):(1/2). However you must achieve at least 40% in both internal coursework and final exam to achieve a clear pass.
Internal coursework assessment: The internal assessment for this course will consist of:
Two tests - 60%
Tutorial assessment - 40%.
Internal assessment/final examination ratio 1:1 Enrolled students can access resources to this paper on i-waikato under Moodle.
The course notes booklet is available for purchase from the Print Shop. The booklet also contains practical exercises. This booklet is an integral component of the paper and it is highly recommended that students purchase it.
All other course material i.e. tutorial exercises and suggested answers to the practical exercises can be accessed by logging onto Moodle. Tutorial exercises plus the relevant data sets will also be available on the R Block Lab server in the 121 Folder.
Utts and Heckard's "Mind on Statistics" by Utts and Heckard (5th ed).
Special consideration for missed work or impaired performance is covered in the University Calendar. Documentary evidence such as a medical certificate should be submitted direction to the Statistics Department Administrator, in Room G.2.18. Academic Integrity Follow this link for Academic Integrity information. Student Concerns and Complaints Follow this link for Student Concerns and Complaints information. |