MATH07025 2013 STATISTICS AND COMPUTING
This course introduces the student to descriptive statistics, probability and probability distributions and statistical inference. The student will also be introduced to the use of an appropriate computing package to perform statistical analysis of data.
Learning Outcomes
On completion of this module the learner will/should be able to;
Graphically display and numerically summarise data using methods of descriptive statistics.
Apply probability and probability distributions to data analysis.
Choose and apply appropriate test of hypotheses.
Use regression methods to analyse relationships between variables.
Use Microsoft Excel (or an equivalent statistical program) to perform statistical analysis of data
Module Assessment Strategies
Assessment of this module will include continuous assessment worth 50% and an end of semester exam worth 50%. The end of semester exam will be employed to assess student's knowledge of statistics. Continuous assessment is designed to give the student an in-depth understanding and knowledge of the capabilities of Microsoft Excel for statistical analysis of data and will include weekly computer based activities/tasks and in-class test assignments.
Module Dependencies
Indicative Syllabus
Descriptive Statistics
Types of data, graphical representation of data. Measurements of central tendency, dispersion and skewness.
Probability
Laws of probability, algebra of events, mutually exclusive events, independent events.
Probability Distributions
Continuous and discrete distributions: binomial, Poisson and normal, use of tables.
Sampling theory
Sampling theory, estimation, point and interval estimates.
Tests of hypothesis
Tests of sample means and sample proportions, z tests and t tests. Chi-squared tests.
Correlation and Regression
Calculation of Correlation coefficient and its significance, Tests for linearity of calibration data; analysis of residuals for linearity testing.
Experimental Design
Random allocation of samples in a study, bias, response bias and use of controls; sampling; summarising data; The partial factorial design for testing ruggedness/robustness of an experiment.
Coursework & Assessment Breakdown
Coursework Assessment
Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
---|---|---|---|---|---|---|
1 | Weekly activities in practicals in computer lab. | Continuous Assessment | UNKNOWN | 25 % | OnGoing | 1,2,3,4,5 |
2 | Continuous Assessment 1. | Continuous Assessment | UNKNOWN | 10 % | Week 6 | 1,2,3,4,5 |
3 | Continuous Assessment 2. | Continuous Assessment | UNKNOWN | 15 % | Week 12 | 1,2,3,4,5 |
End of Semester / Year Assessment
Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
---|---|---|---|---|---|---|
1 | End of Term Exam. | Final Exam | UNKNOWN | 50 % | End of Term | 1,2,3,4 |
Full Time Mode Workload
Type | Location | Description | Hours | Frequency | Avg Workload |
---|---|---|---|---|---|
Lecture | Tiered Classroom | Lecture | 2 | Weekly | 2.00 |
Laboratory Practical | Computer Laboratory | Laboratory Practical | 1 | Weekly | 1.00 |
Independent Learning | UNKNOWN | Self Study | 4 | Weekly | 4.00 |
Module Resources
Reilly, James (2006), Using Statistics, Gill & Macmillan
Owen, Frank (1994), Statistics, Prentice Hall
Bluman, Allan (2007), Elementary Statistics, McGraw Hill
Resources presented on Moodle
Exam centre for end of term exam
None