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. Through real-world data example students will understand the usefulness of statistics in business and marketing.
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 terms that should be explained are sample, population, observer, parameter, inference, margin of error, significance level and confidence level.
- Acquire understanding that different formulas in different contexts, but that the underlying logic is the same.
- Knowledge of the limitations of statistical method.
After completing the course, students will be able to:
- Determine the target level of variables, and be able to perform descriptive analysis based on a sample, with appropriate center and scatter targets and appropriate graphs.
- Describe the covariance between two variables.
- Interpret results of descriptive analysis. Simple probabilities must be calculated.
- 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 printing 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. 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.
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.
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|
Teaching on Campus
Group work / Assignments
Prepare for teaching
Student's own work with learning resources
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.