SSST - Statistics

Statistics

Computer Science

Information Systems

Programme(s) where module is offered

BSc Computer Science with Electrical Engineering
BSc Computer Science with Economics
BSc Computer Science with Management
BSc Computer Science with International Relations
BSc Computer Science with Political Science
BSc Information Systems with Electrical Engineering
BSc Information Systems with Economics
BSc Information Systems with Management
BSc Information Systems with International Relations
BSc Information Systems with Political Science;

Status (core, option, free choice)

Core

FHEQ Level

Unit Value

8 ECTS

Semester taught

Spring

Pre-Requisite Modules or Qualifications

None

Module Code

MATH 260

Module coordinator

Dr. Mirna Udovicic

Applicable From

2016

Educational Aims of the Module

This module will start with graphical interpretation and display of data.
Describing patterns and departures from patters including discussion of central tendencies and variability for both univariate and bivariate data will be studied.
Exploring bivariate data will include correlation, linearity, least-squares regression lines, and residuals analysis if times permits.
Some fundamental topics in probability need to be covered: interpreting probability together with discrete random variables (including binomial and geometric and expected values and variability of those), of continuous random variables such as normal distribution, and sampling distributions.
This module will also cover statistical inference, estimating population parameters, testing hypothesis and confidence intervals.
The module will also touch upon sampling and experimentation techniques of planning and conducting a study.

Module Outline/Syllabus

Statistics, Data, and Statistical Thinking
Methods for Describing Sets of Data
Probability
Random Variables and Probability Distribution
Inferences Based on a Single Sample
Inferences Based on a Two Samples
Design of Experiments and Analysis of Variance
Categorical Data Analysis

Student Engagement Hours

Type	Number per Term	Duration	Total Time
Lectures	15	2 hours	30 hours
Tutorials	15	2 hours	30 hours
Total Guided/Independent Learning Hours			40
Total Contact Hours			60
Total Engagement Hours			100

Assessment Method Summary

Type	Number Required	Duration / Length	Weighting	Timing / Submission Deadline
Final exam	1	180 minutes	50%	End of semester
Mid-term exam	1	90 minutes	20%	Week 8
Project (group)	1	2,000 words	10%	Week 13
Test	2	60 minutes	20%	Weeks 4 and 13

Module Outcomes

Teaching and Learning Strategy:

Interactive lectures on module material.
Tutorials provide series of development exercises and solutions to illustrate the theory.
Group project enables students to develop research skills and apply the gained knowledge on a concrete problem through conducting research, analysing and presenting the data and project results.
Practical demonstrations
Interactive lectures
Weekly Lab exercises with tutor-lead support will allow students to gain hands on skills in data analysis
Group project
Tutorials
Project

Assessment Strategy

Final Exam
Mid-term exam
Test
Project

Key Texts and/or other learning materials

Set text

James T. McClave, P. George Benson and Terry Sincich.(2013) Statistics for Business and Economics, 12th edition, Pearson.

Supplementary Materials

Field. Discovering Statistics using SPSS, 3rd edition, Sage.
Newbold, P., et al., (2015), Statistics for Business and Economics, Pearson
Hand, D., (2008), Statistics: A short Introduction, Oxford University Press
Urdan, T., (2010), Statistics in Plain English, 3rd Edition, Routledge
Scientific Research, (2015), Open Journal of Statistics, [online], http://www.scirp.org/journal/ojs/ (accessed 25th November 2015)

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook. The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : Autumn 2016

Date approved by School Learning and Teaching Committee: 28th September 2016

Date approved by School Board of Study : 12th October 2016

Date approved by University Learning and Teaching Committee: 2nd November 2016

Date of Annual Review: December 2017

SSST Subjects