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STAT7009 - Inferential Statistics

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Title:Inferential Statistics
Long Title:Inferential Statistics
Module Code:STAT7009
 
Duration:1 Semester
Credits: 5
NFQ Level:Intermediate
Field of Study: Statistics
Valid From: Semester 1 - 2019/20 ( September 2019 )
Module Delivered in 4 programme(s)
Next Review Date: March 2023
Module Coordinator: David Goulding
Module Author: Catherine Palmer
Module Description: This module will develop the learner's ability to analyse and understand data through the use of sampling theory and inferential statistics. The emphasis will be practical and will be assisted by a statistical software package.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Generate confidence interval estimates for means, variances and proportions.
LO2 Conduct a variety of hypothesis tests on population parameters.
LO3 Understand the concept of uncertainty in scientific measurements.
LO4 Construct and interpret control charts for variables and attributes.
LO5 Use a statistical software package to carry out hypothesis testing.
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).
13573 STAT6014 Intro Stats for Phys. Sc.
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
 

Module Content & Assessment

Indicative Content
Sampling
Sample statistics and sampling distributions for proportions, means and variances. Central Limit Theorem.
Statistical Inference
Confidence intervals for proportions, means and variances. One and two sample hypothesis tests for means, proportions and variances. Chi-square test of independence.
Control Charts
Construction and interpretation of charts for variable data and for attribute data. Process capability, capability indices.
Measurement Uncertainty
Understand the concepts of systematic and random errors in measurement. Repeatability and reproducibility of measurements.
Software Analysis
The use of statistical software in the application of the various statistical procedures dealt with in the module will be illustrated through a suitable package e.g. Minitab, R, SPSS.
Assessment Breakdown%
Course Work30.00%
End of Module Formal Examination70.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions In-class assessment 1,2,4 15.0 Week 7
Practical/Skills Evaluation Statistical software lab assessment 1,2,4,5 15.0 Week 12
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 70.0 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 3.0 Every Week 3.00
Lab Case study analysis using statistical software 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Study, Solving sample problems 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 2.0 Every Week 2.00
Lab Analysis using statistical software 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Study, Solving sample problems 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
  • James Miller, Jane Miller 2010, Statistics and Chemometrics for Analytical Chemistry [ISBN: 0273730428]
  • Currell, Graham; Dowman, Antony 2009, Essential Mathematics and Statistics for Science [ISBN: 0470694483]
Supplementary Book Resources
  • Michael Sullivan III 2017, Fundamentals of Statistics, 5th Ed., Pearson [ISBN: 978-013450830]
  • Robert V. Hogg, Elliot Tanis and Dale Zimmerman 2014, Probability and Statistical Inference, 9th Ed., Pearson [ISBN: 978-032192327]
  • Montgomery, D.C. & Runger G.C. 2014, Applied Statistics and Probability for Engineers [ISBN: 978-1-118-744]
  • Alan Agresti, Christine A. Franklin and Bernhard Klingenberg 2016, Statistics: The Art and Science of Learning from Data, 4th Ed., Pearson [ISBN: 978-013386082]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SCHQA_8 Bachelor of Science (Honours) in Analytical Chemistry with Quality Assurance 4 Mandatory
CR_SESST_8 Bachelor of Science (Honours) in Environmental Science and Sustainable Technology 4 Mandatory
CR_SCHEM_7 Bachelor of Science in Analytical and Pharmaceutical Chemistry 4 Mandatory
CR_SCHEM_6 Higher Certificate in Science in Chemistry 4 Mandatory

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