STAT8006 - Applied Stats & Probability

Module Details

Module Code: STAT8006
Title: Applied Stats & Probability
Long Title: Applied Stats & Probability
NFQ Level: Advanced
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 2 programme(s)
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 and hypothesis testing using both parametric and non parametric methods.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
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. Describe the structure and compute statistical tests of hypothesis.
LO4 Distinguish between parametric and non parametric methods and decide when non parametric tests should be applied.
LO5 Analyse statistical output from statistical packages.
Dependencies
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 MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
13586 STAT8006 Applied Stats & Probability
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
 
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. The importance and the uses of the Normal distribution. Use of statistical tables.
Sampling and Statistical Inference
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.
Non Parametric methods
Non-parametric versus parametric methods. Typical non-parametric methods: The Sign test, Mann-Whitney Test, Wilcoxon, Spearman’s Rank correlation coefficient.
Module Content & Assessment
Assessment Breakdown%
Coursework40.00%
End of Module Formal Examination60.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Timing Week 5 Learning Outcomes 1,5
Assessment Description
Analyse a data set using descriptive statistics. Submit a report which explains all concepts and procedures used in the analysis.
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Timing Week 11 Learning Outcomes 3,5
Assessment Description
Conduct appropriate hypothesis tests on various data sets. Submit a report which explains all procedures used in the analysis.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3,4,5
Assessment Description
End-of-Semester Final Examination
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 University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of Module Content Every Week 2.00 2
Lab Contact Statistical package lab Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Worksheets Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of Module Content Every Week 2.00 2
Lab Contact Statistical package lab Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Worksheets Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Mario F. Triola. (2012), Elementary Statistics, 12th. [ISBN: 9780321836960].
  • Michael J. Crawley. (2012), The R Book, 2nd. [ISBN: 9780470973929].
  • Tadhg L. O'Shea. (2013), Essential Statistics for Researchers, [ISBN: 9780957505902].
  • Perry R. Hinton. (2004), Statistics Explained: A Guide for Social Science Students, 2nd. [ISBN: 9780415332859].
  • David S. Moore, George P. McCabe, Bruce A. Craig. (2016), Introduction to the Practice of Statistics, 9th. [ISBN: 9781319013387].
Supplementary Book Resources
  • Douglas C. Montgomery, George C. Runger. (2013), Applied Statistics and Probability for Engineers, 6th. [ISBN: 9781118539712].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory
CR_SDAAN_9 Master of Science in Data Science & Analytics 1 Mandatory