| Title: | Intro Stats for Phys. Sc. |
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| Long Title: | Intro Stats for Phys. Sc. |
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| Field of Study: |
Statistics
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| Valid From: |
Semester 1 - 2019/20 ( September 2019 ) |
| Next Review Date: |
March 2023 |
| Module Coordinator: |
David Goulding |
| Module Author: |
Catherine Palmer |
| Module Description: |
This module provides an introduction to data analysis and probability theory. 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 |
Graphically display and numerically summarise data using methods of descriptive statistics. |
| LO2 |
Apply the rules of probability and use probability distributions to model random variables. |
| LO3 |
Model the relationship between two continuous variables using simple linear regression. |
| LO4 |
Use a statistical software package to perform exploratory data analysis and fit simple linear regression models. |
| 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 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).
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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.
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| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Descriptive Statistics
Collection and presentation of data: frequency distributions, histograms, box plots, cumulative frequency, contingency tables. Calculation of summary statistics: measures of central tendency and measures of dispersion.
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Probability
Classical, frequentist and axiomatic definitions. The elementary rules for calculation of probabilities. Independent events, mutually exclusive events, conditional probability, tree diagrams and Bayes' theorem.
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Probability Distributions
Random variables. Discrete and continuous distributions. Properties of probability density and cumulative density functions. Expected value and variance. Binomial, Poisson, and Normal distributions. Use of statistical tables.
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Regression and Correlation
Bivariate relationships, scatter diagrams, coefficient of correlation and coefficient of determination. Simple linear regression and transformation of variables to achieve linearity.
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| Assessment Breakdown | % |
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| Course Work | 30.00% |
| End of Module Formal Examination | 70.00% |
| Course Work |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Short Answer Questions |
In-class test: descriptive statistics and probability. |
1,2 |
15.0 |
Week 7 |
| Practical/Skills Evaluation |
Statistical software lab assessment |
1,2,3,4 |
15.0 |
Week 12 |
| End of Module Formal Examination |
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| 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.
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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 |
Analysis of simple case studies using statistical software |
1.0 |
Every Week |
1.00 |
| Independent & Directed Learning (Non-contact) |
Exercise sheets |
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) |
Exercise Sheets |
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 |
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- Currell, Graham; Dowman, Antony 2009, Essential mathematics and statistics for science, Wiley-Blackwell [ISBN: 0470694480]
| | Supplementary Book Resources |
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- James McClave and Terry Sincich 2018, A First Course in Statistics, 12 Ed., Pearson [ISBN: 9781292165417]
- Allan G. Bluman 2013, Elementary Statistics: A Step by Step Approach, 9 Ed., McGraw-Hill [ISBN: 978-00735349]
- O'Shea, T. L. 2013, Essential Statistics for Researchers, IT Tralee [ISBN: 095750]
- Ross, Sheldon M 2014, Introduction to probability and statistics for engineers and scientists, Elsevier [ISBN: 0123948428]
- Neil J. Salkind 2016, Statistics for People Who (Think They) Hate Statistics, 6 Ed., SAGE [ISBN: 978-150633383]
| | This module does not have any article/paper resources |
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| Other Resources |
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- Website: CIT Department of MathematicsMathsOnline, Accessible via CIT's Virtual Learning Environment
- Website: WolframAlpha
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Module Delivered in
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