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MSCI212: Statistical Methods for Business


Department: Management Science NCF Level: FHEQ/QCF/NQF5//RQF5
Study Level: Part II (any yr) Credit Points: 15.0
Start Date: 09-10-2017 End Date: 15-12-2017
Available for Online Enrolment?: Y Enrolment Restriction: Fully available to all students
Module Convenor:

Syllabus Rules and Pre-requisites

  • The student must take the following modules:
  • The following modules may not be taken:

CMod description

  • At the heart of many real management problems are data that needs to be described, analysed and interpreted. Statistical methods are important across the range of Management School subject areas (e.g. accounting and finance, marketing, economics, operations management and operational research). This module develops your ability to describe, analyse and interpret data soundly, making effective use of computer software.

    Developing these skills will also help you demonstrate to prospective employers that you have practical skills that can immediately be put to good use to solve problems for organisations either in the public or private sector.

    The lecture materials, and the problems you are asked to solve in workshops, reflect the problems that organisations have to solve in practical situations where data analysis skills are required.

     This module runs in weeks 1-10.

Curriculum Design: Outline Syllabus

  • Outline Lecture Plan (Provisional and Approximate)

     

     

    Week

    Topic

    Text Navigation

    Tutorials/

    Workshops

     

    1

    Introduction;

    Understanding data using SPSS.

     

    Chaps 1-6 ‘in practice’

    General Intro

     

    2

    Applications of Probability – model choice and interpretation

     

    Chaps 7-12 ‘in practice’

    Basic SPSS exercises.

     

    3

    Sampling, Sampling Variation and Confidence Intervals -  including Central Limit Theorem

    Chaps 13-15

    Probability  & Discrete & Continuous dist’ns

     

    4

    Statistical Tests and Inference

     

    Chaps 16

    CLT via SPSS

     

    5

    Comparisons: Statistical Inference for two populations.

    Chap 18

    Confidence interval & hypothesis testing.

     

    6

    Alternative Approaches to Inference & Chi-square test of Independence.

    Chaps 17 & 5

    Hypothesis testing in SPSS

     

    7

    Regression Modelling using SPSS: Simple Linear regression

    Chaps 19 & 21

    Uses of non-parametric & Chi-square tests

     

    8

    Regression Modelling using SPSS: Statistical Model Building

    Chaps 23

    Intro to Regression in  SPSS

     

    9

    Regression Modelling using SPSS: Statistical Model Building

    Chap & 24

    Interpreting Regression output

     

    10

    Regression Modelling using SPSS: Regression Diagnostics

    Chaps 22 & 23

     

    Regression using SPSS

     

    There will be 20 lectures and 10 workshops (5 in seminar rooms and 5 in PC labs). The lectures will present the concepts, methods and results, drawing on business applications to illustrate them.

    Practice in using the lecture material is essential and examples will be set which students are expected to attempt prior to workshops. Attendance at workshops is a compulsory and integral part of the course; they will provide students with an opportunity to obtain guidance on those parts of the course where they are having difficulty and also experience of using statistical software.

  • 70% Exam
  • 30% Coursework

Educational Aims: Subject Specific: Knowledge, Understanding and Skills

  •  

    The overall objective of this course is to develop students' abilities to describe, analyse and interpret data soundly, making effective use of computer software. These skills will help students demonstrate to prospective employers that they have practical skills that can immediately be put to good use to solve problems for organisations either in the public or private sector.  The lecture materials and problems that students are asked to solve in tutorials relate to typical problems that organisations have to solve in practical situations where data analysis skills are required.

Learning Outcomes: Subject Specific: Knowledge, Understanding and Skills

  • Subject-specific learning outcomes:

    By the end of the course you should be able to:

    • Know when and how to apply Discrete Probability Distributions (General, Uniform, Poisson and Binomial), including use of probability formulae, tables and SPSS; 
    • Know when and how to apply Continuous Probability Distributions (General, Uniform, Normal and Exponential), including use of probability formulae, tables and SPSS; 
    • Know when and how to apply basic Statistical Inference ideas, including the Central Limit Theorem and confidence intervals; 
    • Know when and how to apply Z and t tests for Hypothesis Testing about the Mean of a Population, manually and using SPSS; 
    • Know when and how to apply Z and t tests for Hypothesis Testing about the Means of two Populations, manually and using SPSS; 
    • Know when and how to apply the Chi-Square Statistic to test for independence; 
    • Know when and how to apply the Nonparametric Statistical Tests (Mann-Whitney, Wilcoxon, Spearman's Rank Correlation) using SPSS; 
    • Know how to fit, check and use Simple Linear Regression models using SPSS; 
    • Know how to fit, check and use Multiple Regression models using SPSS.

    Cognitive abilities/Non-subject-specific learning outcomes:
    By the end of the course you should be able to:

    • Use a statistical computer package well;
    • Appreciate how to apply the appropriate analytical methodologies to solve problems for organisations when data is available;
    • Appreciate the potential for use and misuse of statistical ideas in business situations.

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