MATH07028 2019 Statistics
This module is designed to introduce the learner to the subject of statistics as applied to topics in environmental science. The module will cover basic descriptive statistics, and data presentation before moving on to cover inferential statistics and methods of statistical testing which have particular relevance to environmental science.
On completion of this module the learner will/should be able to;
Demonstrate their knowledge of core statistics skills such as data presentation and use of summary statistics.
Apply their knowledge of descriptive statistics to summarise a dataset in an appropriate and meaningful way.
Use inferential statistics to make robust judgements about a population of interest by analysis of sample data from the population.
Select, apply, and analyse the results of a statistical test relevant to a particular dataset and bearing in mind the nature of the data and the strengths and weaknesses of different tests.
Teaching and Learning Strategies
This module will be delivered full-time. This includes lectures, computer practicals, and will be augmented by independent learning and directed learning. This approach is expected to address student learning needs. Moodle will be used as a repository of educational resources and as a means of assessment (e.g. quizzes, uploading assignments).
Module Assessment Strategies
This module is 70% continuous assessment and 30% final exam.
The continuous assessment will take place in a computer practical lab and will consist of two aspects:
1. Use of statistical software to demonstrate achievement of learning outcomes.
2. Completion of Moodle quizzes to demonstrate knowledge as it is accumulated during the semester. This portion of the CA will take place on a ongoing basis over the semester and will consist of 6 separate Moodle quizzes. Each quiz will be opened when the relevant topic are covered and will remain open thereafter for the rest of the semester.
Repeat continuous assessment and/or final exam.
Demonstrate knowledge of data handling, representation and interpretation
- Data types.
- Entering data into a statistical software package.
- Sorting, filtering scrubbing and formatting data using statistical software.
- Graphical representation and interpretation of data using statistical software: bar charts, trend charts, box plots.
- Working with statistical distributions.
Make use of descriptive statistics to summarise a data set in an appropriate manner
- Generating summary statistics be calculator and statistical software: mean, median, mode, standard deviation, variance, range.
- Representing summary statistics graphically using statistical software: bar chart with error bars, box plot.
Apply inferential statistics to make robust judgements about a population of interest by analysis of sample data from the population
- Sampling schemes and random sampling
- Use of a random number generator for sample selection.
- Generation of confidence intervals, standard error.
- Bivariate linear regression using statistical software.
- t-test, F-test, ANOVA using statistical software.
- Introduction to non-parametric testing.
Choose an appropriate statistical approach (descriptive or inferential) relevant to a particular data set in order to make robust conclusions from it
- Hypothesis testing
- Use and interpretation of t-test, F-test, ANOVA, non-parametric testing using software by p-value approach.
- Application of statistics to project scenarios.
Coursework & Assessment Breakdown
|Title||Type||Form||Percent||Week||Learning Outcomes Assessed|
|1||Short Answer Questions : worksheets, moodle quizzes.||Continuous Assessment||UNKNOWN||30 %||OnGoing||1,2,3|
|2||Practical Evaluation : use of software to analyse statistical problems.||Continuous Assessment||UNKNOWN||40 %||OnGoing||1,2,3,4|
End of Semester / Year Assessment
|Title||Type||Form||Percent||Week||Learning Outcomes Assessed|
|1||Final Exam to test student's achievement of learning outcomes||Final Exam||UNKNOWN||30 %||End of Term||1,2,3|
Full Time Mode Workload
|Lecture||Computer Laboratory||Lecture and Computer Practical||3||Weekly||3.00|
|Independent Learning||UNKNOWN||Self Study||4||Weekly||4.00|
Required & Recommended Book List
2011-03 An Introduction to Statistics Using Microsoft Excel Academic Conferences Limited
ISBN 9781906638559 ISBN-13 1906638551
To help new researchers use statistics from simple descriptive statistics through to the power of inferential statistics. The book is a step by step guide which makes no assumptions about prior knowledge of the subject. There are many worked examples and appropriate diagrams and figures. As Excel functions are used in the exploration of statistics very few mathematical equations are needed. The book contains numerous self tests, exercises and assignments and appropriate solutions are available on the web. This makes the book both student and teacher friendly.
2002-03-12 Practical Statistics for Environmental and Biological Scientists Wiley
ISBN 0471496650 ISBN-13 9780471496656
All students and researchers in environmental and biological sciences require statistical methods at some stage of their work. Many have a preconception that statistics are difficult and unpleasant and find that the textbooks available are difficult to understand. Practical Statistics for Environmental and Biological Scientists provides a concise, user-friendly, non-technical introduction to statistics. The book covers planning and designing an experiment, how to analyse and present data, and the limitations and assumptions of each statistical method. The text does not refer to a specific computer package but descriptions of how to carry out the tests and interpret the results are based on the approaches used by most of the commonly used packages, e.g. Excel, MINITAB and SPSS. Formulae are kept to a minimum and relevant examples are included throughout the text.
2008-06-09 Student Projects in Environmental Science Wiley
ISBN 047084566X ISBN-13 9780470845660
Research projects are among the core components of many undergraduate and Masters degrees within environmental science and physical geography, and students increasingly undertake courses in quantitative research methodology before starting on their own assignment. This one-stop-shop text guides students through their research project from the initial stages of choosing a suitable topic, of conducting the relevant experiments and interpreting the data, through to effective presentation of the results. Takes a case-study approach to illustrate the range of environmental science topics, with cases supplied by specialists in the field. Practical worked examples and self-assessment tasks illustrate key statistical and mathematical points so as to keep heavy theory to a minimum Covers software such as Excel, SPSS and mathematical modelling, and includes statistical tables
Excel with Data Analysis Toolpak