Short Title:Statistics for Marketing Purposes
Full Title:Statistics for Marketing Purposes
Module Code:DMKT H2012
 
ECTS credits: 5
NFQ Level:6
Module Delivered in 2 programme(s)
Module Contributor:Colm McGuinness
Module Description:This module will expose students to fundamental statistical techniques, and develop analytical problem solving skills.
Learning Outcomes:
On successful completion of this module the learner will be able to
  1. Summarize large sets of data, including grouped data, using the standard measures of central tendency and dispersion and their definitions and properties, and represent it graphically.
  2. Determine the nature and strength of linear relationships within bi-variate data using least squares techniques and product moment correlation, and create seasonally adjusted forecasts from time series data.
  3. Apply the laws of probability, in particular the concept of a random variable and its distribution, the meaning of expected values, and the properties of common distributions such as the normal, binomial, Poisson and exponential distributions.
  4. Interpret the concept of a statistic as a random variable arising from sample data, with the central limit theorem determining the behaviour of such statistics and thereby underpinning the idea of a statistical test.
  5. Frame and use an appropriate test for a simple statistical problem, based on their knowledge of hypothesis testing and the central limit theorem.
  6. Implement techniques learnt using a modern computer package, such as Excel or SPSS.
 

Module Content & Assessment

Indicative Content
Basic mathematics
Mathematical notation: Sigma notation and subscripts. Rounding & accuracy. Equation of a line and understanding of slope. Use of a calculator. Absolute and relative error. Percentages. Calculation order (BOMDAS). Basic manipulation of equations (reverse BOMDAS).
Data Collection & Presentation
Sampling techniques & Bias. Basic sample size. Collection & classification of data. Primary and secondary data. Continuous and discrete data. Graphical and tabular representation of data. Histograms, polygons and curves. Simple and grouped frequency distributions. Class boundaries, widths, mid-points, scaling factors, scaled frequency, cumulative frequencies, percentage frequencies. Cumulative and percentage frequency distributions. Non-numeric frequency distributions and charts.
Data Analysis
Averages, mean, median and mode of grouped and ungrouped data. Diagrammatic and formulaic determination of the median and other quantiles, and the mode. Measures of dispersion, standard deviation, range, inter-quartile range, coefficient of variation. Concept of skewness.
Regression Techniques
Method of least squares for time series and other data. Linear relationship modelling. Moving averages for time series. Time series trend & adjusted seasonal components. Additive and multiplicative time series models. Deseasonalised data. Correlation. Scatter diagrams. Strong, weak, positive, negative and spurious correlation. Rank correlation.
Basic Probability
Basic understanding of probability as a proportion. Contingency tables and Venn diagrams. Conditional and unconditional probability. Mutually exclusive, complementary, independent and dependent events. General and basic addition rules and multiplication rules. Application of probability to reliability and systems. Permutations and combinations. Bayes’ Theorem, and Bayesian statistics. Contingency tables and decision trees. Expected value. Expected versus most likely values.
Probability Distributions
Introduction to random variables and probability distributions for them. Discrete and continuous variables. Expected value. Binomial distribution. Poisson distribution. Uniform distribution. Normal distribution, and standard normal tables. Inverse normal problems, confidence intervals and control charts. t-distribution for "small" sample sizes, and corresponding confidence intervals. Central limit theorem. t-Test for a single mean: Correct interpretation of p values in terms of evidence, sample size, and power.
Indicative Assessment Breakdown%
Course Work Assessment %30.00%
Final Exam Assessment %70.00%
Course Work Assessment %
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation Students will be required to complete a practical exam using a spreadsheet application (eg Excel). 1,3,5,6 15.00 Week 11
Open-book Examination Students will be required to complete ongoing practice questions. 1,3,4,5 15.00 Every Week
Final Exam Assessment %
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination None 70.00 End-of-Semester
Indicative 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.

ITB reserves the right to alter the nature and timings of assessment

 

Indicative Module Workload & Resources

Indicative Workload: Full Time
Frequency Indicative Average Weekly Learner Workload
Every Week 3.00
Every Week 1.00
Every Week 4.00
Resources
Recommended Book Resources
  • Francis, A. & Mousley, B. 2014, Business Mathematics and Statistics, 7th Ed., Cengage Learning [ISBN: 1408083159]
Supplementary Book Resources
  • McClave, J. T., Benson, P. G., Sincich, T. L. 2013, Statistics for Business and Economics, 12th Ed., Pearson [ISBN: 1292023295]
  • Jon Curwin, Roger Slater, Quantitative Methods, Thomson Learning [ISBN: 1844809056]
  • Reinhart, A. 2015, Statistics Done Wrong: The Woefully Complete Guide, No Starch Press [ISBN: 1593276206]
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
BN_BDMKT_8 Bachelor of Arts (Honours) in Digital Marketing 3 Mandatory
BN_BDMKT_7 Bachelor of Arts in Digital Marketing 3 Mandatory