STATS 1005  Statistical Analysis & Modelling 1
North Terrace Campus  Semester 2  2017

General Course Information
Course Details
Course Code STATS 1005 Course Statistical Analysis & Modelling 1 Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week Available for Study Abroad and Exchange Y Prerequisites At least a C in both SACE Stage 2 Mathematical Studies and SACE Stage 2 Specialist Mathematics; or a 3 in International Baccalaureate Mathematics HL; or MATHS 1013 Incompatible STATS 1000, STATS 1004, STATS 1504, ECON 1008 Restrictions Not suitable for BCompSc or BEng(Software Engineering) students Course Description This is a first course in Statistics for mathematically inclined students. It will address the key principles underlying commonly used statistical methods such as confidence intervals, hypothesis tests, inference for means and proportions, and linear regression. It will develop a deeper mathematical understanding of these ideas, many of which will be familiar from studies in secondary school. The application of basic and more advanced statistical methods will be illustrated on a range of problems from areas such as medicine, science, technology, government, commerce and manufacturing. The use of the statistical package R will be developed through a sequence of computer practicals.
Topics covered will include: basic probability and random variables, fundamental distributions, inference for means and proportions, comparison of independent and paired samples, simple linear regression, diagnostics and model checking, multiple linear regression, simple factorial models, models with factors and continuous predictors.Course Staff
Course Coordinator: Professor Patricia Solomon
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.

Learning Outcomes
Course Learning Outcomes
 Understand the foundations of basic probability, random variables, and expectation and variances of random variables and their linear combinations.
 Understand hypothesis testing for one sample, two sample, and ANOVA. To be able to fit linear models to data and use these to predict future observation.
 Be able to take data and describe it statistically and to use approriate graphics to visualise patterns in the data.
 Be familiar with R and how to use it to perform a basic analysis of data.
 Understand the importance of statistics in modern scientific research.
 Appreciate the mathematical underpinnings of statistics.
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)
1,2,3,4,5,6 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
1,2,3,4,5,6 
Learning Resources
Required Resources
NoneRecommended Resources
Moore, McCabe, and Craig: Introduction to the Practice of Statistics, 6th EditionOnline Learning
This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that the students make appropriate use of these resources.
Link to MyUni login page:
https://myuni.adelaide.edu.au/webapps/login/ 
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides the assessment opportunities for students to gauge their progress and understanding.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 36 72 Tutorials 12 18 Assignments 5 48 Practicals 12 18 TOTALS 156 Learning Activities Summary
Lecture Outline
1. Set theoretic probability. Important definitions.
2. Conditional probability, independent events, Bayes’ theorem.
3. Random variables, probability mass function, probability density, cumulative distribution function.
4. Means and variances.
5. Independent random variables.
6. Covariance and correlation.
7. Linear combinations of independent random variables.
8. Binomial distribution.
9. Normal distribution.
10. Quantile plots.
11. Inference for a single normal mean; review of z.
12. Review of significance and confidence.
13. Introduction of t.
14. Mean and variance of sample mean from the linear combinations formula.
15. Mean and variance of sample mean from a simple random sample in a finite population.
16. Two independent samples.
17. Two independent samples vs paired data.
18. Nonparametric methods for means and proportions.
19. The simple linear regression model.
20. Derivation of least squares estimates.
21. Least squares estimates as linear combinations of the data.
22. Residuals and model checking for simple linear regression.
23. Transformations and simple linear regression.
24. Prediction for simple linear regression with and without transformation.
25. Multiple linear regression, principle of least squares.
26. Interpretation of coefficients.
27. Prediction for multiple linear regression. Diagnostics for multiple linear regression. Collinearity. Multiple vs simple regression.
28. The oneway layout and ANOVA.
29. Analysis for the oneway layout via multiple regression with indicator variables. The nointeraction model for two factors.
30. Factorial experiments vs block designs.
31. Parallel and nonparallel regression models.
32. Categorical data, basic tests for proportions.
33. Independence for the r × s contingency table.
34. General goodness of fit tests.
35. Review lecture.
36. Review lecture
Tutorial Outline
1. Probability, random variables.
2. Means, variance, covariance, correlation.
3. Linear combinations, binomial, normal.
4. Inference for single normal mean, significance, confidence.
5. Ttest, sample mean.
6. Two sample testing.
7. Linear regression.
8. Linear regression II.
9. Multiple linear regression, ANOVA.
10. Multiple linear regression II.
11. Oneway ANOVA.
12. Factorial design.
Practical Outline
1. Introduction, data input, basic descriptive statistics and graphics.
2. Customised graphics. SPSS menu commands and code.
3. Probability calculation.
4. Quantile plots and onesample tprocedures
5. Illustration of sampling properties via simulation.
6. Two sample t procedures.
7. Simple linear regression.
8. Diagnostics from linear regression
9. Multiple regression.
10. Diagnostics for multiple regression.
11. Simple factorial models.
12. Parallel and nonparallel regression models. 
Assessment
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.
Assessment Summary
Component Weighting Outcomes Assessed Assignments 30% All Exam 70% All Assessment Related Requirements
Aggregate score of at least 50%Assessment Detail
Assessment Item Distributed Due Date Weighting Assignment 1 Week 1 Week 3 6% Assignment 2 Week 3 Week 5 6% Assignment 3 Week 5 Week 7 6% Assignment 4 Week 7 Week 9 6% Assignment 5 Week 9 Week 11 6% Submission
All written assignments are to be submitted to the designated hand in boxes within the School of Mathematical Sciences with a signed cover sheet attached.
Late assignments will not be accepted.
Assignments will have a two week turnaround time for feedback to students.
Course Grading
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 149 Fail P 5064 Pass C 6574 Credit D 7584 Distinction HD 85100 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.

Student Feedback
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 ongoing 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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