Analysis of Data (Bologna) STATS4065

  • Academic Session: 2023-24
  • School: School of Mathematics and Statistics
  • Credits: 8
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No

Short Description

The course gives students further experience of analyzing data in a wide variety of contexts, using the R computer package.

Timetable

Requirements of Entry

This course is only available to students on the Double Degree programme in Statistics with the University of Bologna.

Excluded Courses

Statistics 3A: Data Analysis [STATS3011]
Data Analysis [STATS4052]
Data Analysis (Level M) [STATS5018]

Co-requisites

-/-

Assessment

End-of-course practical test in computer lab, carried out in accordance with the assessment procedures and regulations of the University of Bologna.

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

This course aims

■ to provide students with experience in implementing statistical models (including linear models) using R, in a wide variety of contexts;

■ to develop expertise in developing appropriate modelling strategies and interpreting the results; and

■ to develop written communication skills.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ implement simple statistical techniques, such as the normal linear model, in R;

■ interpret the results from statistical procedures and draw appropriate conclusions;

■ develop and implement an appropriate modeling approach to answer questions of interest about a given data set;

■ critically assess the quality of a statistical analysis conducted by someone else; and

■ write up the results of a statistical analysis concisely in the form of a report.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.