Module Code: H8STATS
Long Title Statistics
Title Statistics
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator:  
Module Author: Isabel O'Connor
Departments: School of Computing
Specifications of the qualifications and experience required of staff


Master’s and/or PhD degree in 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 Explain the principles and uses of descriptive statistics and inferential statistics.
LO2 Use Principles of statistical Inquiry
LO3 Carry out analyses based on descriptive and inferential statistics within a business context
LO4 Demonstrate the usage of methodologies applied in prediction (forecasting)
LO5 Use and understand software tools for business data analysis (e.g. SPSS, R, Excel)
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/Data Presentation
Arrangement, pre-processing and representation of data Measures of central tendency (mode, median, mean) Measures of dispersion (range, variance, standard deviation) Scales of Variables Statistical graphics & figures (e.g., pie chart, bar chart)
Inference Statistics
Standard Errors Hypothesis Testing Parametric Tests (e.g., T-Test, ANOVA, regression) Non-parametric Tests (e.g., chi-square tests)
Prediction/Forecasting
Simple Linear Regression Correlation Smoothing and filtering of data Nature of time series
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: Formative 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 provided on the in-class individual or group activities.
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
Assessment will consist of week graded tutorials to carry out statistical analysis on sample data sets using tools such as Excel, R, and SPSS.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
End-of-Semester Final Examination
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
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.

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 No Description 24 Per Semester 2.00
Tutorial No Description 12 Per Semester 1.00
Independent Learning No Description 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
Workload: Online
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 12 Per Semester 1.00
Tutorial No Description 12 Per Semester 1.00
Directed Learning No Description 12 Per Semester 1.00
Independent Learning No Description 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 No Description 24 Per Semester 2.00
Tutorial No Description 12 Per Semester 1.00
Independent Learning No Description 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • James T. McClave,Terry T. Sincich. Statistics, Global Edition, 13th Edition. [ISBN: 9781292161556].
  • Neil J. Salkind. (2016), Statistics for People Who (Think They) Hate Statistics (International Student Edition), Sage Publications, Incorporated, p.552, [ISBN: 9781506361161].
Supplementary Book Resources
  • Maindonald John,. (2008), , Using R for data analysis and graphics, r-project, Introduction, code and commentary, http//cran.
  • Andy Field, 2013,. Discovering Statistics Using IBM SPSS Statistics, 4th, Sage Publications Inc, London, p.915,.
  • McClave, James T., Benson, George & Sincich, Terry,. (2013), , Statistics for Business and Economics, 12th, Prentice Hall.
  • Peter Dalgaard. (2008), Introductory Statistics with R, Springer Science & Business Media, p.364, [ISBN: 9780387790534].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: Approved on behalf of SoC to allow for approval of parent programmes.