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Module Descriptor School of Computer Science and Statistics

Module CodeST1002
Module NameST1002 STATISTICAL ANALYSIS I
Module Short Title
ECTS5
Semester TaughtMichaelmas term
Contact HoursLecture: 2 hours per weekLab hours:1 hour per week Total hours:33 hours
Module PersonnelAssociate Professor  Myra O’ Regan
Learning Outcomes

To explain basic statistical theory and apply the techniques to data.  Students should be able to describe and interpret the results in a detailed fashion. More precisely students should be able to  

  • Explain the nature of data      
  • Generate appropriate descriptive  statistics Illustrate data with appropriate graphical techniques
  • Calculate simple probabilities
  • Understand how various statistical distributions are used Select a random sample
  • Create estimates and confidence intervals of population parameters from samples
  • Carry out and interpret the results of  statistical tests including      
    • Independent t-tests      
    • Chi-square test
  • Explain the ideas behind simple linear regression  
Learning Aims

The aim of the course is to introduce the students to basic statistical concepts. There will be considerable emphasis on the use of a statistical package to analyse data.  

Module Content
  • Nature of data
  • Descriptive statistics
  • Displaying data using graphs
  • Laws of probability
  • Bayes Rule
  • Binomial Distribution
  • Poisson Distribution
  • Exponential Distribution
  • Normal Distribution
  • Select random sample
  • Confidence intervals for means and proportions
  • Hypothesis testing
  • Independent t-tests
  • Chi-Square tests
  • Simple linear regression  
Recommended Reading List
  1. Stuart, M. An Introduction to Statistical Analysis for Business and Industry A problem Solving approach.  London: Hodder Arnold, 2003
  2. Moore, D.S, McCabe G.P & Craig, B.A. An Introduction to the practice of Statistics 6th ed.  New York: W. H. Freeman, 2009  
Module PrerequisitesNone
Assessment Details

Assessment is by written examination (contributing 80% to the overall mark) and a series of assessments to be completed (contributing 20% to the overall mark). To pass the module, students must achieve a mark of 40% in both the written examination and the continuous assessment components.Class and lab attendance is compulsory. Students will be required to attend 80% of labs and lectures. Non attendance will result in an additional project to complete. Students will work together in groups. to complete labs. In the supplemental examinations, assessment is by written examination only, which contributes 100% of the overall mark. 

Module Website
Academic Year of Data2016/17