MET 3431 Statistics
MET 3431 Statistics
The course provides an introduction to statistical thinking. The student first learns how a sample can be collected and summarized and how we use such summaries to interpret the data. Then, using various statistical methods, we learn to generalize from the sample to the population. The student learns to construct confidence intervals and to carry out hypothesis tests, and how these should be interpreted. The focus is on interpretation of results, often of printouts from software and conceptual understanding. Relatively little emphasis will be placed on mathematical procedures. Through many examples from real life, students should be able to see the relevance and areas of use for statistics in marketing and management subjects. Use of statistical software is an important element of the course.
During the course, students will:
- Acquire a broad knowledge of the central statistical terms and understand how statistical analysis takes place from the collection of data, via descriptive analysis to generalization to the population.
- Acquire knowledge of the normal distribution and the t-distribution and knowledge of the central limit theorem and why this knowledge is necessary to justify the use of various statistical methods to analyze data. The student should also acquire knowledge of various statistical methods for analyzing data. Finally, the student should acquire knowledge of the elementary use of statistical data tools and understand that the use of statistical data tools is necessary to be able to effectively process and analyze data.
- Gain knowledge of the limitations of statistical methods.
It is a goal that the course should enable the students to be able to plan and carry out investigations using the most commonly used statistical methods. Students should be able to interpret analysis results from, for example, reports or computer printouts. After completing the course, students should be familiar with being able to use computer tools to process and analyze data.
- The students' ability for analytical thinking and an ability to reflect on results and calculations should be strengthened by completing the course.
- To understand that statistical methods can easily be misused.
- Taking anonymity and privacy into account when collecting data.
- To know that there are other branches within the statistics subject, such as econometrics and machine learning.
- Collection of data. Variables and measurement level.
- Descriptive analysis of the sample.
- Covariation.
- Simple probability theory with focus on the normal distribution.
- Confidence intervals for mean and proportion.
- Hypothesis testing for average and proportion.
- Simple correlation- and regression analysis.
- Selected central tests including the chi-square test for covariation between two categorical variables.
The course is carried out with 48 course hours which will consist of ordinary lectures where the syllabus is reviewed and problem solving (including problems which must be solved with computer tools). The software SAS JMP is integrated into the teaching and the students will, through task solving, also actively process and analyze data using statistical software on their own.
Solving problems will be a central part of the joint lectures where the students are presented with tasks in the lecture and receive feedback by solving, reviewing and discussing these.
For each theme, a work program will be drawn up with literature references and task sets. The student must acquire the material in the literature reference and solve the tasks.
Mandatory work requirements where 3 of 5 work requirements must be passed to take the exam.
The final exam will be based on that the student has solved the tasks and mandatory work requirement given in the course.
E-Learning
Where the course is delivered as an online course, the lecturer will, in collaboration with the study administration, arrange an appropriate combination of digital learning resources and activities. These activities will correspond to the stated number of teaching hours delivered on campus. Online students are also offered a study guide that will provide an overview of the course and contribute to course progression. The total time students are expected to spend completing the course also applies to online studies.
Re-sit examination
Students who do not get the work requirement approved in the course are not allowed to take the exam. This means that they must take the entire course again when completing it later.
Students who do not pass the written examination or wish to improve their grade may re-take the examination in connection with the next scheduled examination.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
No specific prerequisites required.
| Mandatory coursework | Courseworks given | Courseworks required | Comment coursework |
|---|---|---|---|
| Mandatory | 5 | 3 | Throughout the semester, 5 compulsory work requirements will be given where the use of software is central. These are answered in Itslearning. Each test is either approved or not approved. You are given the opportunity to take the test again if it is not passed on the first attempt. It is mandatory that the student has passed at least 3 of these 5 tests in order to take the exam. |
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 100 Grouping: Individual Support materials:
Duration: 5 Hour(s) Exam code: MET34311 Grading scale: ECTS Resit: Examination every semester |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 48 Hour(s) | |
Group work / Assignments | 81 Hour(s) | |
Prepare for teaching | 66 Hour(s) | |
Examination | 5 Hour(s) |
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.