Statistical Data Analysis and Interpretation
Introduction
The old adage ‘what gets measured improves’ is reflected in the dramatic increase in the range and scope of data being collected today. We are barraged with statistics on sports results, economic indicators and politics: people are becoming familiar with scoring averages, inflation rates and voter satisfaction surveys. The advent of low-cost personal computers combined with the widespread availability of powerful computing software, such as Excel, means that many people have both large data sets and powerful tools with which to analyse them.
In this report, a data set collected by the author, in an observational study, is analysed. The data set contains three variables;
Project Score; the score accumulated by each student prior to sitting the end of term exam.
Attendance Score; calculated as a percentage of term lectures attended.
Final Score; the score accumulated by each student and calculated using the weighted average of the project and exam scores.
The analysis considers a variety of different methods to represent and reduce the data-set, the results presented are discussed briefly and the most appropriate family of measures for the data-set identified.
Organisation providing Data Source
The University of Limerick was established in 1972 as the National Institute for Higher Education: Limerick, and achieved university classification in 1989. The university, now in its 40th year, provides graduate and postgraduate education to over eleven thousand students.
Students are offered a wide range of modules, delivered by teaching staff in the twenty-eight departments spread across four faculties.
An important function of the teaching staff is the grading of student submissions and the calculation of final grades. The university gives discretion to teaching staff in respect of these tasks, enabling them to determine how best to assess student learning. It is important that staff reduce and represent grading data in a manner that ensures
The old adage ‘what gets measured improves’ is reflected in the dramatic increase in the range and scope of data being collected today. We are barraged with statistics on sports results, economic indicators and politics: people are becoming familiar with scoring averages, inflation rates and voter satisfaction surveys. The advent of low-cost personal computers combined with the widespread availability of powerful computing software, such as Excel, means that many people have both large data sets and powerful tools with which to analyse them.
In this report, a data set collected by the author, in an observational study, is analysed. The data set contains three variables;
Project Score; the score accumulated by each student prior to sitting the end of term exam.
Attendance Score; calculated as a percentage of term lectures attended.
Final Score; the score accumulated by each student and calculated using the weighted average of the project and exam scores.
The analysis considers a variety of different methods to represent and reduce the data-set, the results presented are discussed briefly and the most appropriate family of measures for the data-set identified.
Organisation providing Data Source
The University of Limerick was established in 1972 as the National Institute for Higher Education: Limerick, and achieved university classification in 1989. The university, now in its 40th year, provides graduate and postgraduate education to over eleven thousand students.
Students are offered a wide range of modules, delivered by teaching staff in the twenty-eight departments spread across four faculties.
An important function of the teaching staff is the grading of student submissions and the calculation of final grades. The university gives discretion to teaching staff in respect of these tasks, enabling them to determine how best to assess student learning. It is important that staff reduce and represent grading data in a manner that ensures
Bibliography: Anderson, D. Sweeney, D. Williams, T. Freeman, J. Shoesmyth, E. (2010) Statistics for Business