Statistics 1A: Applied Statistics STATS1010

  • Academic Session: 2018-19
  • School: School of Mathematics and Statistics
  • Credits: 20
  • Level: Level 1 (SCQF level 7)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes

Short Description

This is an introductory statistics course for students from a wide range of disciplines covering concepts and methods useful for students in their other University courses. The emphasis of this non-mathematical course is on the application and interpretation of statistics.

Timetable

Lectures (1 hour): Monday, Tuesday, Wednesday and Thursday at 12.00 pm

Practicals: weekly for two hours at times to be arranged.

Requirements of Entry

Mandatory Entry Requirements

Pass in Standard Grade Mathematics (or equivalent)

Excluded Courses

STATS1002 Statistics 1Y: Probability and Statistical Methods
STATS1003 Statistics 1Z: Statistics in Action

Assessment

Assessment

 

One written examination (75%); practical laboratory work (25%).

 

Reassessment

In accordance with the University's Code of Assessment reassessments are normally set for all courses which do not contribute to the honours classifications. 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 are listed below in this box.

 

Reassessment will not be available for any coursework items.

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? No

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

■ to introduce students to statistical concepts and thinking;

■ to provide a practical introduction to data analysis;

■ to demonstrate the importance and practical usefulness of statistics;

■ to encourage and equip students to apply simple statistical techniques in an appropriate statistical computer package in order to analyse and interpret studies in a wide range of disciplines;

■ to enable students to communicate the results of their analyses in clear non-technical language in writing up reports;

■ to make students aware of the limitations of simple techniques and encourage them to seek expert advice when more complex procedures are required.

Intended Learning Outcomes of Course

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

 

■ describe the key concept of variability;

■ explain the ideas of population, sample, parameter, statistic and probability;

■ differentiate between common types of data, especially continuous and categorical data (paired and independent), and display them appropriately;

■ apply the basic concepts of normal, binomial and multinomial distributions;

■ identify and describe the basic concepts of simple sampling schemes;

■ identify and describe some basic issues in questionnaire design;

■ determine the sample size required in simple studies to estimate and compare means and proportions;

■ produce and interpret interval estimates for means and proportions and simple prediction intervals;

■ carry out and interpret a variety of commonly used hypothesis tests (for continuous and categorical data) and report results in non-technical language;

■ calculate and interpret the sample correlation coefficient between two variables and provide a test of zero correlation;

■ fit a linear regression model with one or more explanatory variables and carry out appropriate tests to assess the usefulness of such models;

■ check assumptions underlying various methods of analysis and describe alternative approaches when assumptions are not met;

■ analyze and interpret data from all of the above techniques using an appropriate statistical computing package.

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.