| Module Code: |
H8STATS |
|
Long Title
|
Statistics
|
|
Title
|
Statistics
|
| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| 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 | % |
|---|
| Coursework | 50.00% |
| End of Module Assessment | 50.00% |
AssessmentsFull 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 |
|
| 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. |
|
