Module Details
Module Code: |
DATA S7Z01 |
Full Title:
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Statistics and Data Analysis
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Valid From:: |
Semester 1 - 2018/19 ( September 2018 ) |
Language of Instruction: | English |
Module Owner:: |
Arjan van Rossum
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Module Description: |
The aim of this module is to teach the student how to apply statistical methodology in the solution of practical problems and to make them aware of the key role of statistical methodology in the design and analysis of scientific and industrial experiments and investigations.
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Module Learning Outcome |
On successful completion of this module the learner will be able to: |
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Module Learning Outcome Description |
MLO1 |
Calculate, present and interpret numerical and graphical summaries of statistical data. |
MLO2 |
Recognise the key role of data collection and probabilistic methods in statistical inference and be able to apply simple probability models. |
MLO3 |
Describe the concept of sampling variation and be able to construct and interpret confidence intervals and tests of hypotheses in the one- and two-sample cases, including paired investigations. |
MLO4 |
Understand the key assumptions that underlie the standard statistical analyses and be able to diagnose violations of these assumptions. |
MLO5 |
Appreciate the key role of statistical methodology in scientific investigations and be able to summarise and communicate the results of statistical analysis in a non-
technical language. |
MLO6 |
Develop a proficiency in the use of a Statistics' package and be familar with the use of computer simulations, both as a learning tool in Statistics and as a method of quantifying sampling variation. |
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 DkIT 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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55089 |
DATA S7Z01 |
Statistics and Data Analysis |
Module Indicative Content |
Descriptive Statistics
Tally charts and frequency distributions. Symmetrical and Skewed Distributions. Barcharts, histograms, boxplots, dotplots and scatterplots. Measures of central tendency and dispersion including five-number summaries. Accuracy and precision of measurements.
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Probability
Approaches to probability. Events, sample spaces, random variables and probability distibutions. Addition and multiplication laws. Normal, hypergeometric, binomial and poisson models. Computer simulations of probabilities and sampling variation. Normal probability plots.
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Confidence Intervals
Sampling Variation. Estimation of percentages and means in the large and small sample cases. Z and t based intervals. Checking assumptions underlying confidence intervals,
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Hypothesis Testing
Hypothesis testing of percentages and means in the large sample and small sample cases. Z and t based tests in the one-sample and two-sample and paired cases. Checking assumptions underlying hypothesis tests.
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Analysis of cross-classified tables.
Display of data in tables. Two-by-two and r x c tables. Chi-square test for independence and goodness of fit.
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Software Applications
Implement all of the above methods using Minitab. Also carry out simulations as an alternative to performing tests and as a learning tool in Statistics.
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Module Assessment
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Assessment Breakdown | % |
Course Work | 30.00% |
Final Examination | 70.00% |
Module Special Regulation |
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AssessmentsFull Time On Campus
Reassessment Requirement |
A 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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Reassessment Description Repeat assessments only in the case of excused absence.
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DKIT reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time On Campus |
Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
Contact |
Main content delivery. |
Every Week |
3.00 |
3 |
Practical |
Contact |
Minitab Laboratory |
Every Week |
1.00 |
1 |
Independent Study |
Non Contact |
No Description |
Every Week |
8.00 |
8 |
Tutorial |
Contact |
Practice problems. |
Every Week |
1.00 |
1 |
Total Weekly Learner Workload |
13.00 |
Total Weekly Contact Hours |
5.00 |
This module has no Part Time On Campus workload. |
Module Resources
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Recommended Book Resources |
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David M Diez, Christopher D Barr, Mine Çetinkaya-Rundel. (2012), Open Intro Statistics, 2nd. 1 to 6, https://www.openintro.org/, Available under a Creative Commons License.
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Mullins, Eamonn. (2003), Statistics for the Quality Control Chemistry Laboratory, Royal Society of Chemistry.
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Stuart, Michael. (2003), An Introduction to Statistical Analysis for Business and Industry, Arnold, London:.
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Moore D.S. & McCabe G.. (1989), Introduction to the Practice of Statistics, Freeman and Co., New York.
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Colin Weatherup. (2007), Experimental Statistics Using Minitab, Arima publishing..
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Reilly, James. (2006), Using Statistics, Gill and Macmillan,.
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Walpole, R., Myers, R. Myres, S. (2006), Probability and Statistics for Engineers & Scientists, 8th. Prentice Hall.
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Townsend, John. (2002), Practical Statistics for Environmental and Biological Scientists, Wiley.
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R.C. Campbell. (1989), Statistics for Biologists, 3rd edition. Cambridge University Press.
| This module does not have any article/paper resources |
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Other Resources |
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http://en.wikipedia.org/wiki/statistics.
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Website, stats4stem,
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Website, https://www.khanacademy.org/#Statistics,
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Website, https://onlinecourses.science.psu.edu/st
at414/node/3,
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Website, http://www.seeingstatistics.com/,
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Website, http://www.dur.ac.uk/stat.web/,
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Website, http://wiki.stat.ucla.edu/socr/index.php
/EBook,
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Website, http://stattrek.com/ap-statistics/practi
ce-test.aspx,
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Website, http://www.coursera.org,
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