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 we learn to generalize from the sample to the population. The student learns to construct confidence intervals and to perform hypothesis tests, and how these should be interpreted. The focus is on understanding concepts, and on interpreting results, with less focus on mathematical procedures. Through many examples from reality, the student should be able to see the relevance of and uses for statistics in marketing and economics. Use of statistical software is absolutely crucial.
During the course the students will:
- Acquire broad knowledge of the central statistical concepts and understand how statistical analysis takes place from data collection, through descriptive analysis to generalization to the population.
Examples of concepts which shall can be explained are sample, population, statistic, parameter, inference, margin of error, significance level, confidence level and probability distribution. - Knowledge of the central limit theorem
- Knowledge of the limitations of statistical method.
After completing the course, students will be able to:
- Determine the level of measurement of variables, and be able to perform descriptive analysis based on a sample, with appropriate measures for central trend and variation and appropriate graphs.
- Describe the covariance between two variables.
- Interpret results of descriptive analysis.
- Calculate simple probabilities - especially using the normal distribution.
- Be able to construct and interpret the most commonly used confidence intervals, and perform key hypothesis tests.
- Use statistical software and be able to interpret printouts from the software.
- Present the results of the analyzes in an easy-to-understand language.
- The student should be aware that statistical methods may be easily misused and misinterpreted.
- To take into account anonymity and privacy when collecting data
- Collection of data
- Descriptive analysis of the sample
- Covariation
- Simple probability theory with focus on the normal distribution
- Confidence intervals for mean and proportion
- Hypothesis tests for mean and proportion
- Simple correlation- and regression analysis
- Selected central tests, including the chi-square test for covariation between two categorical variables.
The course consists of 48 hours of lectures. The software SAS JMP will be a central part of the teaching.
In lectures, theory will be illustrated by using multiple data sets often in companion with SAS JMP.
It is mandatory work requirements where 4 of 6 work requirements must be passed to take the exam.
The final exam will be based on that the student has solved the coursework requirement throughout the semester. A part of the final exam will be based directly on the exercises given in the mandatory work requirements.
E-learning
In course delivery as online courses, lecturer will, in collaboration with the student administration, organize an appropriate course implementation, combining different learning activities and digital elements on the learning platform. Online students are also offered a study guide that will contribute to progression and overview. Total recommended time spent for completing the course also applies here.
Re-sit examination
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
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 | 6 | 4 | During the course of the semester 6 mandatory assignments with software will be given. These are submitted on Itslearning. Each assignment is assessed as either pass or fail. The student needs at least 4 passes in order to take the final 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 | 50 Hour(s) | |
Prepare for teaching | 42 Hour(s) | Working with SAS JMP |
Student's own work with learning resources | 40 Hour(s) | |
Examination | 20 Hour(s) | 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.