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STAT8006 - Applied Stats & Probability

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Title:Applied Stats & Probability
Long Title:Applied Statistics and Probability
Module Code:STAT8006
 
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2014/15 ( September 2014 )
Module Delivered in 1 programme(s)
Module Coordinator: AINE NI SHE
Module Author: Sean Lacey
Module Description: This module will apply statistics and probability distributions to modern day problems. It will develop graphical visualisation methods, probability theory and distributions. The module will develop knowledge, skill and competence of sampling theory, hypothesis testing and linear regression.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Graphically display and numerically summarise data using methods of descriptive statistics.
LO2 Apply the rules of probability and use probability models for data analysis.
LO3 Compute and interpret point and interval estimates of population parameters. Determine required sample sizes.
LO4 Describe the structure and compute statistical tests of hypothesis.
LO5 Interpret scatterplots, correlation coefficients and the results from simple linear regression. Use the results of linear regression for prediction.
LO6 Analyse statistical output from statistical packages such as IBM SPSS and R.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Data collection and presentation
Collection and presentation of data. Basic descriptive statistics (both graphical and numerical).
Probability
Relative frequency and axiomatic definitions. Laws of probability, conditional probability, independent and mutually exclusive events.
Probability distributions
Random variables. Discrete and continuous distributions. Properties of probability density and cumulative density functions. Binomial, Poisson, normal and exponential distributions. Use of statistical tables.
Sampling
Sampling distributions of proportions and means. Calculate the required sample size to obtain confidence intervals of required length for a single parameter. Confidence intervals and hypothesis tests for: one-sample mean and proportion; difference between two-sample means and proportions.
Linear regression
Interpret scatterplots, correlation coefficients and the results from simple linear regression. Use the results of linear regression for prediction.
Statistical packages
Analyse statistical output from statistical packages such as IBM SPSS and R.
Assessment Breakdown%
Course Work50.00%
End of Module Formal Examination50.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation Descriptive statistics 1,2,6 25.0 Week 6
Practical/Skills Evaluation Confidence intervals and hypothesis testing 3,4,5,6 25.0 Week 11
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4,5,6 50.0 End-of-Semester
Reassessment Requirement
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.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Formal lecture 2.0 Every Week 2.00
Lab Statistical package lab 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Worksheets 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Formal lecture 1.5 Every Week 1.50
Lab Statistical package lab 1.5 Every Week 1.50
Independent & Directed Learning (Non-contact) Worksheets 4.0 Every Week 4.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Mario F. Triola,, Elementary Statistics [ISBN: 0321782682]
  • Tadhg L. O'Shea 2013, Essential Statistics fo Researchers [ISBN: 0957505902]
  • Perry R. Hinton 2004, Statistics explained, Routledge [ISBN: 9780415332859]
  • Colin Gray, Paul R Kinnear, IBM SPSS Statistics 19 Made Simple [ISBN: 9781848720695]
  • David S. Moore, George P. McCabe, Bruce Craig, Introduction to the Practice of Statistics & CD-Rom [ISBN: 978-1429286640]
Supplementary Book Resources
  • Douglas C. Montgomery, George C. Runger, Applied Statistics and Probability for Engineers [ISBN: 978-0470053041]
  • David S. Moore, The basic practice of statistics [ISBN: 978-1429224260]
  • B. Burt Gerstman, Basic Biostatistics Pkg w/ 2009 Formulas and Tables [ISBN: 978-0763781347]
  • Jim Fowler, Phil Jarvis, and Mel Chevannes, Practical statistics for nursing and health care [ISBN: 978-0471497165]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie