Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Tutorial
Module Offerings
4100STATS-JAN-CTY
Aims
To enable the student to carry out an exploratory analysis of a set of data either 'by hand' or using appropriate software. This will include building knowledge of a simple relational databases in order to interrogate and filter data for analysis.
To provide the student with the required background knowledge of probability and random variables so that they can make use of a number of formal statistical models in their analyses.
To enable the student to appreciate the need for, and use of, confidence intervals in a number of commonly occurring data analysis situations.
To enable the student to appreciate the need for, and use of, hypothesis tests in a number of commonly occurring data analysis situations.
Learning Outcomes
1.
Extract data using simple relational database techniques.
2.
Carry out an exploratory numerical and graphical analysis of a set of data by hand and/or using appropriate software.
3.
Calculate and estimate probabilities and confidence intervals for parameters of a number of probability models used in data analysis.
4.
Construct and carry out hypothesis tests upon parameters of a number of probability models in data analysis.
Module Content
Outline Syllabus:Data and tabular display. Graphical displays - pie charts, histograms, stem-and-leaf plots, box plots, scatter diagrams.
Sample summary statistics - mean, median, mode, quartiles, interquartile range, variance, standard deviation.
Basic introduction to relational databases to extract appropriate data for exploratory analysis using appropriate software.
Samples and populations.
Probability - definitions, addition rule, multiplication rule, independent events, conditional probability.
Random variables - discrete and continuous.
Expectation of a random variable.
Probability distributions - discrete uniform, Bernoulli, Binomial, Poisson, continuous uniform, Normal, Normal probability plots.
Sampling distribution of the mean, central limit effect, Normal approximations Confidence intervals - the mean of a Normal population: one and two sample cases, large and small sample methods, the t-distribution.
Hypothesis testing - the mean of a Normal population: one and two sample cases, large and small sample methods, testing equality of variances.
Module Overview:
This module covers the exploratory analysis of datasets (including basic relational database skills to extract data from appropriate sources), the use of probability to handle uncertainty and develops techniques of hypothesis testing and confidence interval construction.
This module covers the exploratory analysis of datasets (including basic relational database skills to extract data from appropriate sources), the use of probability to handle uncertainty and develops techniques of hypothesis testing and confidence interval construction.
Additional Information:This module covers the exploratory analysis of datasets (including basic relational database skills to extract data from appropriate sources), the use of probability to handle uncertainty and develops techniques of hypothesis testing and confidence interval construction.