Module Code: H6STATS1
Long Title Statistics I
Title Statistics I
Module Level: LEVEL 6
EQF Level: 5
EHEA Level: Short Cycle
Credits: 10
Module Coordinator: EUGENE O'LOUGHLIN
Module Author: EUGENE O'LOUGHLIN
Departments: School of Computing
Specifications of the qualifications and experience required of staff


Master’s degree in a computing or cognate discipline. May have industry experience also.

 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Evaluate and choose between different options for inference statistics so that a motivated decision between two or more options can be made
LO2 Develop a strategy for a statistical analysis when presented with a real- world problem from business
LO3 Apply methodologies used in prediction and interpret the results
LO4 Use and compare software tools for business data analysis (e.g. SPSS, R, Excel, SAS)
LO5 Critically evaluate statistical applications in a particular discipline
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).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

See section 4.2 Entry procedures and criteria for the programme including procedures recognition of prior learning
 

Module Content & Assessment

Indicative Content
Descriptive Statistics (Describing and Charting Data Sets)Ethics
Arrangement, pre-processing and representation of data. Measures of central tendency (mode, median, mean) . Measures of dispersion (range, variance, standard deviation) . Statistical graphics & visuals (e.g., box-plot, histograms). Ethics implications and Statistics.
Introduction to Probability
Sample points, sample space, events. Calculating probabilities. Venn diagrams. Combinatorial mathematics
Hypothesis Testing
Null/Alternative Hypothesis. Single sample z test. One-tail tests. Two-tail tests
Test for Normality
Normal distributions . Q-Q/P-P Plots. Shapiro-Wilk Test. Kolmogorov-Smirnov Test
Independent Samples Test
Test for Equality of Variance. Student’s t-Test (independent samples)
Dependent Samples Test
Student’s t-Test (Dependent samples)
One-Way Analysis of Variance (ANOVA)
One-way ANOVA. Post Hoc tests
Non-parametric tests
Mann-Whitney Test. Wilcoxon Sign-Rank Test
Non-parametric tests
Kruskal-Wallis Test. Chi squared (χ2) test for independence
Reporting
Sample size. Confidence intervals. Effect size. Power
Correlation Linear Regression
Pearson’s correlation coefficient. Scatter Diagrams. Prediction. Simple Linear Regression. Multiple Linear Regression
Revision
Revision
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be included by the provision of exercises and short answer questions during weekly tutorials. Feedback will be provided individually or as a group in written and/or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning. Learners will be encouraged to share exercises for peer review – in particular for data visualisations.
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,5
Non-Marked: No
Assessment Description:
The first test will assess learners’ knowledge and understanding of setting null and alternative hypotheses for single sample and two sample statistical tests. Learners will also be required in the test to calculate test statistics (z and t), and to report on results. A sample question, marking scheme, and solution, is provided below.
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 3,4,5
Non-Marked: No
Assessment Description:
The second test (lab-based) will assess learners’ knowledge and understanding of specific statistical tests (e.g., ANOVA, Chi-Square, Mann-Whitney, Kruskal-Wallis). A sample question, marking scheme, and solution, is provided below.
No End of Module Assessment
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
The repeat strategy for this module is an examination. Learners will be afforded an opportunity to repeat the examination at specified times throughout the year and all learning outcomes will be assessed in the repeat exam.

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 Classroom & Demonstrations (hours) 24 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 36 Per Semester 3.00
Independent Learning Independent learning (hours) 190 Per Semester 15.83
Total Weekly Contact Hours 5.00
 

Module Resources

Recommended Book Resources
  • Neil J. Salkind. (2014), Statistics for People Who (Think They) Hate Statistics (4th ed), Sage Publications, Inc Thousand Oaks.
  • Cortinhas, C. & Black, K.. (2012), Statistics for Business and Economics, John Wiley & Sons.
  • McClave, J & Sincich, T.. (2012), Statistics (12th ed), Pearson.
Supplementary Book Resources
  • Field, A.. (2013), Discovering Statistics Using IBM SPSS Statistics (4th ed), Sage Publications Inc London.
  • McClave, James T., Benson, G., & Sincich, T.. (2013), Statistics for Business and Economics (12th ed), Prentice Hall.
  • Coolidge, F. L.. (2012), Statistics (3rd ed), Sage Publications.
  • Winston, L. W.. (2014), Microsoft Excel.
  • Urdan, T. C.. (2016), Statistics in Plain English (4th ed), Routledge.
This module does not have any article/paper resources
Other Resources
Discussion Note: