Full Title:Statistics and Data Analysis
Language of Instruction:English
Module Code:DATA S7Z01
 
Credits: 7.5
Valid From:Semester 1 - 2013/14 ( September 2013 )
Module Delivered in 6 programme(s)
Module Description:The aim of this module is to teach the student how to apply statistical methodology in the solution of practical problems and to make them aware of the key role of statistical methodology in the design and analysis of scientific and industrial experiments and investigations.
Learning Outcomes:
On successful completion of this module the learner should be able to
  1. Calculate, present and interpret numerical and graphical summaries of statistical data.
  2. Recognise the key role of data collection and probabilistic methods in statistical inference and be able to apply simple probability models.
  3. Describe the concept of sampling variation and be able to construct and interpret confidence intervals and tests of hypotheses in the one-sample, two-sample, more than two samples and paired studies.
  4. Understand the key assumptions that underlie the standard statistical analyses and be able to diagnose violations of these assumptions.
  5. Appreciate the key role of statistical methodology in scientific and industrial experiments and be able to summarise and communicate the results of statistical analysis in a non- technical language.
  6. Develop a proficiency in the use of a Statistics' package and be familar with the use of computer simulations as a learning tool in Statistics.
 

Module Content & Assessment

Indicative Content
Descriptive Statistics
Tally charts and frequency distributions. Normal and Skewed Distributions. Barcharts, histograms, boxplots and dotplots and scatterplots. Measures of central tendency and dispersion including five-number summaries. Accuracy and precision of measurements. Repeatability and Reproducibility.
Probability
Approaches to probability. Events, sample spaces, random variables and probability distibutions. Addition and multiplication laws. Normal, hypergeometric, binomial and poisson models. Computer simulations of probability densities and distributions. Sampling variation. Normal probability plots.
Tests of Hypothesis and Confidence Intervals
Estimation and hypothesis testing for percentages and means in the large sample and small sample case. Z and t tests and intervals in the one-sample, two-sample and paired cases. Checking assumptions underlying tests and confidence intervals,
Analysis of cross-classified tables.
Display of data in tables. Two-by-two and r x c tables. Chi-square test for independence and goodness of fit.
Analyis of Variance.
Principles of experemental design. One and two way ANOVA. Main effects and interactions. F-tests and residual plots.
Software Applications
Implement all of the above methods using Minitab and/or R. Also carry out simulations as an alternative to performing tests and as a learning tool in Statistics.
Assessment Breakdown%
Course Work30.00%
End of Module Formal Examination70.00%

Full Time

Course Work
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Class Test Class test approximately at half way stage. 1,2,3,4 15.00 0 0 Week 7 0
Class Test Class test at end of module. 1,2,3,4 15.00 0 0 Week 13 0
No Project
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Formal Exam End-of-Semester Final Examination 1,2,3,4 70.00 0 0 End-of-Semester 0
Reassessment Requirement
A repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Repeat assessments only in the case of excused absence but opportunity to gain extra credit in exam.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload & Resources

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 4.00 Every Week 4.00
Practical No Description 1.00 Every Week 1.00
Independent Study No Description 8.00 Every Week 8.00
Total Weekly Learner Workload 13.00
Total Weekly Contact Hours 5.00
This course has no Part Time workload.
Resources
Recommended Book Resources
  • Walpole, R., Myers, R. Myres, S 2006, Probability and Statistics for Engineers & Scientists, 8th Ed., Prentice Hall
  • Reilly, James 2006, Using Statistics, Gill and Macmillan,
  • Townsend, John 2002, Practical Statistics for Environmental and Biological Scientists, Wiley
  • G. B. Wetherill & 1990, Statistical Process Control: Theory and Practice, Chapman & Hall,
  • Mullins, Eamonn 2003, Statistics for the Quality Control Chemistry Laboratory, Royal Society of Chemistry
  • D. Bissell 1994, Statistical Methods for SPC and TQM, Chapman & Hall
  • Colin Weatherup 2007, Experimental Statistics Using Minitab, Arima publishing.
  • Townsend, John 2002, Practical Statistics for Environmental and Biological Scientists, Wiley
  • Stuart, Michael 2003, An Introduction to Statistical Analysis for Business and Industry, Arnold London:
  • R.C. Campbell 1989, Statistics for Biologists, 3rd edition Ed., Cambridge University Press
  • Moore D.S. & McCabe G. 1989, Introduction to the Practice of Statistics, Freeman and Co. New York
Supplementary Book Resources
  • Walpole, R., Myers, R. Myres, S 2006, Probability and Statistics for Engineers & Scientists, 8th Ed. Ed., Prentice Hall
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
DK_SENVI_8 Bachelor of Science (Honours) in Environmental Bioscience 4 Mandatory
DK_SAPBI_7 Bachelor of Science in Applied Bioscience 4 Mandatory
659 Bachelor of Science in Environmental Bioscience 4 Mandatory
DK_SPHAR_7 Bachelor of Science in Pharmaceutical Science 4 Mandatory
Dk_SPHAR_6 Higher Certificate in Science 4 Mandatory
Dk_SAPBI_6 Higher Certificate in Science 4 Mandatory