Business
MA109: Statistics for Business (5 ECTS)
This module follows on from MA119 Mathematics for Business and continues to equip students with mathematical and statistical skills required in Business, Economics, Finance and Marketing. The statistics content in the module suffices as a first course in descriptive statistics, probability theory and sampling theory, and serves to equip students with the tools they need to progress to inferental statistics in MA217 Statistical Methods for Business.
Taught in Semester(s) II. Examined in Semester(s) II.
Workload: 41 hours (36 Lecture hours, 5 Tutorial hours).
Module Learning Outcomes.
On successful completion of this module the learner should be able to:
Mathematics content:
Statistics content:
- define and recognise in application the terms; individual, experimental unit, variable types, population and sample, parameter and statistic, distuingish between applications in decriptive statistics and inferential statistics.
- calculate numerical summaries and construct graphical summaries of qualitative data, such as frequencies, proportions, percentages, bar charts, pie charts, and pareto charts, and interpret those summaries.
- calculate numerical summaries of quantitative data, in terms of central tendency, such as mean, median, mode, in terms of spread, such as the range, standard deviation, variance, percentiles, inter-quartile range, and construct graphical summaries of quantitative data such as histrograms, boxplots, dotplots, stem-and-leaf plots, using these to discuss the shape/distrubution of data, by recognising symmetry versus skewness and outlier detection.
- calculate numerical summaries and construct graphical summaries for the relationships between variables in a sample, such as two-way contingency tables for two qualitative variables, the correlation coefficient and scatterplots for two quantitative variables and intrepret those summaries.
- define probability, sample space, sample points, events, and calulate probabilities using counting theory, including permutations and combinations.
- define and illustrate basic probability rules such the additive rule, multiplication rule, Bayes rule and properties such as when events are mutually exclusive/disjoint and when events are independent.
- define a random variable, expectation and variance of a random variable, calculate probabilities and percetiles for common probability distributions, such as uniform distribution, binomial distribution, poisson distribution, normal distribution, t-distribution, F-distribution, $\chi^2$ distribution.
- discuss methods in data collection and sampling, such as in surveys and experiments, decribe various sampling techniques, experiment designs, the importance of randomisation, probabilistic and non-probablistic sampling methods, and possibility of sampling bias.
Indicative Content
Module Resources
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