Module Code: H8STATS2
Long Title Statistics II
Title Statistics II
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: Sophie Flanagan
Module Author: ORLA LAHART
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Master’s and/or PhD degree in a numerate / scientific discipline, with experience in practical applications of statistical techniques. May have industry experience also
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse and select the appropriate statistical methodology to solve data analysis problems, or make predictions
LO2 Understand the concepts of normality, independence, and homoscedasticity for the selection of statistical tests and forecast technique
LO3 Critically evaluate the outcome of statistical significant tests using advanced concepts, such as statistical power, sample size, and multiple comparisons
LO4 Conduct advanced statistical analyses to answer real life questions and demonstrate ability to solve problems.
LO5 Interpret and clearly communicate the results of statistical tests to take informed decisions using data in the appropriate contexts.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
68040 H8STATS1 Statistics I
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

N/A
 

Module Content & Assessment

Indicative Content
Inferential statistics revisited
Hypothesis testing. One-way ANOVA, and t-test; discussion of tools and programming languages for statistics, such as Python, R, and SPSS
Exploratory data analysis
Correlations, Chi-Square test of independence, box plots, confidence intervals, simple linear regression
Regressions
Multiple linear and polynomial regressions, robust and quantile regressions, stepwise regression, and model selection
Normality
Tests and plots for normality, including QQ-plots, Shapiro-Wilk, and Kolmogorov-Smirnov tests, and Box Cox transformation. Reporting results
Statistical power & sample size
Cohen’s d and Hedges’s g, and other effect size suggestions. Power calculations
Two-way ANOVA
Review of main assumptions. Data preparation. Conduct, interpret, and report ANOVA results
Post-hoc tests
Multiple comparisons and p-value inflation. Tukey’s HSD and Bonferroni correction. Dunn’s and Dunnett's tests. False discovery rate. Reporting results
Non-parametric tests on contingency tables
One and two-factor analysis using Chi-squared test for count data. Reporting results
Non-parametric tests on populations
Mann-Whitney, Wilcoxon, and Kruskal-Wallis tests. Reporting results
Factor Analysis and PCA
Collinearity. Kaiser-Meyer-Olkin test. Screeplot. Loadings and interpretation. Reporting results
Time Series Analysis
Smoothing data and seasonality. Forecasting with Holt-Winters and ARIMA. Relationship between time series forecasting and supervised learning
Survival Analysis
Censored data, Kaplan-Meier estimator, and Cox model
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: Assignment 1 % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
In this assignment the student will prepare data for two-way ANOVA, and any two other non-parametric tests from Week-8 and Week-9. The student may use Python, R, or SPSS, but should not rely on only one tool, variety is expected. It is not necessary to replicate any test you carry out, ie if you perform a test in R it is not necessary to repeat it in SPSS and/or Python. A data file from the Census of Ireland is suggested, though students are permitted to choose a different file if they wish (subject to approval by Lecturer). The task is to prepare a statistical report based on the data in the file
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,5
Non-Marked: No
Assessment Description:
The end of semester examination paper which is two hours in duration. Usually learners are requested to answer four out of five questions. Question format will usually be of essay-style but may also include other formats (e.g., a plan for an extended business data analysis project or a technical figure). Marks will be awarded based on clarity, structure relevant examples, depth of topic knowledge and an understanding of the potential and limits of solutions
No Workplace Assessment
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.
Reassessment Description
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.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Weekly lecture 24 Per Semester 2.00
Tutorial Weekly tutorial 12 Per Semester 1.00
Independent Learning Time Research & autonomous student learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Weekly lecture 24 Per Semester 2.00
Tutorial Weekly tutorial 12 Per Semester 1.00
Independent Learning Time Research & autonomous student learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • David Spiegelhalter. (2019), The Art of Statistics, Pelican, p.256, [ISBN: 0241258766].
  • Bernard Rosner. (2015), Fundamentals of Biostatistics, 8th. Cengage Learning, USA, [ISBN: 978-1-305-26892-0].
  • Peter Dalgaard. Introductory Statistics with R, Springer, p.364, [ISBN: 0387790535].
Supplementary Book Resources
  • Andy Field. (2018), Discovering Statistics using IBM SPSS Statistics, 5th. SAGE Publications, Incorporated, p.775, [ISBN: 9781544328225].
  • EMC Education Services. (2015), Data Science and Big Data Analytics, John Wiley & Sons, Incorporated, [ISBN: 111887613X].
  • Eugene Demidenko. (2019), Advanced Statistics with Applications in R, 10, John Wiley & Sons, p.880, [ISBN: 978-1-118-38798-6].
Recommended Article/Paper Resources
Other Resources
  • [Website], Choosing the correct stat test.
    https://stats.idre.ucla.edu/other/mult-p kg/whatstat/
  • [Website], The Khan Academy. http://www.khanacademy.org/.
  • [Website], Learn with Dr Eugene O’Loughlin. http://www.youtube.com/eoloughlin.
  • [Website], Central Statistics office. http://www.cso.ie.
  • [Website], Glossary of Statistical Terms. http://bit.ly/LIRYpQ.
  • [Website], HyperStat Online Statistics Textbook. http://davidmlane.com/hyperstat/.
  • [Website], The R Project for Statistical Computing. http://www.r-project.org/.
Discussion Note: