STATS 7061 - Statistical Analysis
North Terrace Campus - Semester 1 - 2018
General Course Information
Course Code STATS 7061 Course Statistical Analysis Coordinating Unit School of Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact 1 week intensive - a minimum of 36 hours Available for Study Abroad and Exchange Y Prerequisites C&ENVENG 7043 Assumed Knowledge elementary statistics (mean, variance, histogram) Course Description General introductory course on mathematical statistics. Summary statistics and statistical inference. Histograms and sample statistics. Probability and probability distributions. Detailed coverage of Gaussian (normal) distribution and the lognormal distribution. Sampling distributions and tests of significance. Analysis of variance. Multiple variables with emphasis on the bivariate case. Correlation and regression. Bayes' theorem and introduction to Bayesian statistics. Gy's sampling theory for the sampling of particulate materials.
Course Coordinator: Andrew Metcalfe
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesStudents who successfully complete the course should:
1. understand the concept of a frequency distribution for sample data, and be able to summarise the distribution by diagrams and statistics,
2. understand the principles of probability and the concept of probability distributions,
3. become familiar with binomial, Poisson, normal and log-normal probability distributions,
4. understand linear combinations of random variables and the Central Limit Theorem,
5. understand the concepts of confidence intervals and hypothesis tests,
6. be able to make statistical comparisons of means (paired and unpaired samples), proportions and variances,
7. understand the concepts of ANOVA and be familiar with one-way, two-way, and two-way with interaction ANOVA,
8. understand correlation and regression, and be able to make predictions and understand their limitations,
9. understand the concept of sample preparation error within a geostatistical sampling context.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
all Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
all Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
all Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
Required ResourcesCourse notes: Statistical Analysis by P.A. Dowd.
Recommended Resources1. Statistics and Data Analysis in Geology (3e), J.C. Davis, Wiley 2002
2. Geostatistics for Natural Resources, P. Goovaerts, Oxford 1997
3. Geostatistics for Evironmental Scientists, R. Webster, M.A. Oliver, Wiliey 2007
4. Statistical Methods for Engineers, G.G. Vining, S. Kowalski, Duxbury 2010
5. Probability and Statistics for Engineering and the Sciences (8e), J. Devore, Thomson
6. The R Book, M.J. Crawley, Wiley 2007
Online LearningThe lectures are given over a single week and it is essential to follow these up through MyUni. The MyUni site includes: lecture slide shows; reordings of the lectures; assignments; data; past examination papers; and a discussion board.
Learning & Teaching Activities
Learning & Teaching ModesThe course is taught over five days through a programme of lectures interspersed with tutorial exercises. Six written assignments are distributed at the end of the week and provide opportunities for students to gauge their progress and understanding. The submitted solutions to the assignments form part of the assessment. There is also a written examination.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 26 78 Tutorial exercises 10 10 Assignments 6 68 Total 156
Learning Activities SummaryLectures (L) and tutorial exercises (T)
L1 Variables and data summaries
L2 Measures of location
L3 Measures of dispersion
L4 Populations and probability
L5 Binomial and Poisson distributions
L6 Gaussian (normal) distribution
T2 Normal distribution
L7 Log-normal distribution
L8 Three parameter log-normal distribution
L9 Relationships between normal and log-normal distributions
T3 Log-normal distribution
L10 Estimation and confidence intervals
L11 Hypothesis testing
L12 Chi-square distribution
T4 Chi-square distribution
L13 Student's t-distribution
L15 One way ANOVA
L16 Two way ANOVA
L18 Correlation and regression
L20 Multivariate distributions
L21 Bivariate normal distribution
L22 Regression in the bivariate normal distribution
L23 Maximum likelihood estimation
L24 Bayesian analysis
L25 Sampling errors for particulates and Gy's formula
T10 Gy's formula
Small Group Discovery ExperienceThe class is divided into small groups, between 2 and 5, for the 10 tutorial exercises.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Component Weighting Objectives assessed Assignments 50% all Examination 50% all
Assessment Related RequirementsAn aggregate mark of at least 50% is required for a pass.
Assessment item Distributed Due date Weighting Assignment 1 27 February 27 March 9% Assignment 2 27 February 27 March 8% Assignment 3 27 February 27 March 8% Assignment 4 27 february 27 March 8% Assignment 5 27 February 27 March 8% Assignment 6 27 February 10 April 9%
Submission1. Assignments can either be posted to:
School of Mathematical Sciences
Level 6, Ingkarni Wardli Building
North Terrace Campus
The University of Adelaide
or sent as a PDF to:
(The Microsoft Word file format can render differently on different computers, especially with different add-ons. Should you not be able to convert Microsoft Word files to PDF in the Microsoft Word program itself, please use The University of Adelaide's free PDF conversion form at: http://pdfwizard.adelaide.edu.au/home.asp)
2. A School of Mathematical Sciences cover sheet should be attached to your assignments.
3. Assignments may be submitted at any time before the due date.
4. Assignments will be marked and feedback given within one week of submission.
5. If you need extensions beyond the due date, please make a request before the due date.X
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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