MET 3431 Statistics
MET 3431 Statistics
This course is an introduction to statistical thinking. Firstly, the student will learn to produce and to interpret descriptive statistics. Secondly, the student will learn the logic of statistical inference and how to construct confidence intervals and perform hypothesis tests. The emphasis is on understanding concepts and interpretation of results, more than on mathematical machinery.
The student will learn the most central concepts underlying statistical methodology, from the collection of data to inference about the population. The underlying logic behind the diversity of methods will be perceived. Concepts such as random sampling, population, parameters and statistics, inference, margin of error and levels of significance and confidence should be understood. Through real-world data example students will understand the usefulness of statistics in business and marketing. However, the student should understand the limitations on conclusions drawn from data.
kills in descriptive statistics are to determine level of measurement, and to choose and calculate measures of center and spread, and to produce graphs, for a given sample. Covariation among variables should also be described. The student should be able to understand and interpret descriptive statistics. Students should be able to perform simple probability calculations. The student should be able to construct and understand confidence intervals and perform statistical tests. The student should become familiar with statistical software, and be able to interpret output from such software. The student should be able to report the results of statistical analysis in an easy-to-understand language.
The student should be aware that statistical methods may be easily misused and misinterpreted. It is important that the judgment required for statistical analysis is fair and just.
- Collection of data
- Describing the sample at hand
- Confidence intervals for mean and proportion
- Hypothesis tests for mean and proportion
- Correlation and regression
- Chi-square test
The course consists of 48 hours of lectures, including 4 hours of demonstration of statistical software. The problems studied in class and given as homework assignments will serve as a basis for the final examination.
For each week there will be given a work program with literature references and assignments. In lectures and SAS JMP exercises, theory will be illustrated by using multiple data sets and associated tasks. The final exam will be based on that the student has solved all these tasks throughout the semester.
When course is delivered online, lecturer, in cooperation with the Academic Servises Network, will organize an appropriate combination of digital teaching and lectures. Online students are also offered a study guide to contribute to progression and overview. Total recommended time spent for completing the course also applies here.
Students that have not gotten approved the coursework requirements, must re-take the exercises during the next scheduled course.
Students that have not passed the written examination or who wish to improve their grade may re-take the examination in connection with the next scheduled examination.
Higher Education Entrance Qualification.
No specific prerequisites required.
|Mandatory coursework||Courseworks given||Courseworks required||Comment coursework|
|Mandatory||8||5||In the course of the semester 8 mandatory multiple-choice assignments will be given. These are submitted on Itslearning. Each assignment is assessed as either pass or fail. The student needs at least 5 passes in order to take the final exam.|
|Comment coursework:||In the course of the semester 8 mandatory multiple-choice assignments will be given. These are submitted on Itslearning. Each assignment is assessed as either pass or fail. The student needs at least 5 passes in order to take the final exam.|
|Exam category||Weight||Invigilation||Duration||Support materials||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
Examination every semester
|Form of assessment:||Written submission|
|Support materials:|| |
|Resit:||Examination every semester|
Group work / Assignments
Prepare for teaching
Working with SAS JMP (or some statistical software)
Student's own work with learning resources
Exam incl. preparations.
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.