# Foundation Statistics - MAST0025

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## Module delivery information

This module is not currently running in 2024 to 2025.

## Overview

Statistical techniques are a fundamental tool in being able to measure, analyse and communicate information about sets of data. Using illustrative data sets we show how statistics can be indispensable in applied sciences and other quantitative areas. This module covers the basic methods used in probability and statistics using Excel for larger data sets. A more detailed indication of the module content follows.

Sampling from populations. Data handling and analysis using Excel. Graphical representation for the interpretation of univariate and bivariate data; outliers. Sample summary statistics: mean, variance, standard deviation, median, quartiles, inter-quartile range, correlation. Probability: combinatorics, conditional probability, Bayes' Theorem. Random variables: discrete, continuous; expectation, variance, standard deviation. Discrete and continuous distributions: Binomial, discrete uniform, Normal, uniform. Sampling distributions for the mean and proportion. Hypothesis testing: one sample, mean of Normal with known variance and proportion, 1- and 2-tail. Confidence intervals: one sample, mean of Normal with known variance and population proportion.

## Details

### Contact hours

Contact hours: 48
Private study: 152
Total: 200

## Method of assessment

80% examination, 20% coursework

## Indicative reading

The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.

See the library reading list for this module (Canterbury)

## Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:

1 demonstrate understanding of the basic body of knowledge associated with elementary probability and statistics;
2 demonstrate the capability to solve problems in accordance with the basic theories and concepts in the following areas, whilst demonstrating a reasonable level of skill in
calculation and manipulation of the material: interpretation of data, conditional probability, discrete and continuous probability distributions, hypothesis testing and confidence
intervals;
3 apply the basic techniques associated with hypothesis testing and confidence intervals in several well-defined contexts;
4 make appropriate use of Excel;
5 demonstrate a proficiency in probability and statistics suitable for Stage 1 entry.

## Notes

1. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
2. The named convenor is the convenor for the current academic session.
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