MATH07002 2008 STATISTICS & COMPUTING

General Details

Full Title
STATISTICS & COMPUTING
Transcript Title
STATISTICS AND COMPUTING
Code
MATH07002
Attendance
N/A %
Subject Area
MATH - Mathematics
Department
ASCI - Applied Sciences
Level
07 - NFQ Level 7
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2008 - Full Academic Year 2008-09
End Term
9999 - The End of Time
Author(s)
Padraig McGourty
Programme Membership
SG_SAGRI_B07 201800 Bachelor of Science in Science in Agri-Food Science SG_SAGRI_H08 201800 Bachelor of Science (Honours) in Science in Agri-Food Science
Description
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 the main computing packages used for statistical analysis.

Learning Outcomes

On completion of this module the learner will/should be able to;

1.

Graphically display and numerically summarise data using methods of descriptive statistics.

2.

Apply probability and probability distributions to data analysis.

3.

Choose and apply appropriate test of hypotheses.

4.

Use regression methods to analyze relationships between variables.

5.
Use SPSS and Microsoft Excel to perform statistical analysis of data.

Module Dependencies

Prerequisites
Mathematics for Biologists
Co-requisites
None
Incompatibles
None

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.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
50 %
End of Semester / Year Formal Exam
50 %

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 5 1,2,3,4,5
3 Continuous Assessment 2. Continuous Assessment UNKNOWN 15 % Week 10 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 1,2,3,4,5
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 2 Weekly 2.00
Laboratory Practical Computer Laboratory Laboratory Practical 1 Weekly 1.00
Independent Learning UNKNOWN Self Study 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Module Resources

Non ISBN Literary Resources

Cann, J. Alan (2003), Maths from Scratch for Biologists, Wiley

Reilly,  James (2006), Using Statistics, Gill & Macmillan

Owen, Frank (1994), Statistics, Prentice Hall

Bluman, Allan (2007),  Elementary Statistics, McGraw Hill
Other Resources
None