MET 1190 Statistics
MET 1190 Statistics
The course MET 1190 provides a basic introduction to the subject of statistics. The content of the course can be divided into two main themes, data analysis and probability theory. Data analysis is the activity of processing and analyzing sampled data for the purpose of drawing conclusions (inference) about phenomena in a population. Probability theory forms the mathematical foundation for developing the methods used in data analysis. Data analysis and probability theory are becoming increasingly important in areas such as economics, financial economics, and accounting. The course is composed of topics such as descriptive statistics, probability theory, classic statistical inference, and regression analysis. The course will also include training in the use of statistical software and simple programming.
After going through the learning process described in this course description, students will:

Know basic concepts in probability theory, such as "random experiment", "sample space", "event", "probability" and "conditional probability".

Know terms related to random variables, such as "expectation", "variance", "probability distribution", "linear combination of random variables" and "random sample".
 Know different probability models for discrete and continuous random variables, as well as the central limit theorem.

Know concepts from classical statistics, such as "estimator", "confidence interval", "hypothesis testing" and "pvalue".
 Know procedures for studying the relationship between two variables like "scatter plots", "covariance/correlation" and "regression analysis".
 Know how to use statistical software and simple programming routines
After completing the learning process described in this course description, students will:

Be able to perform descriptive statistical analysis.
 Be able to apply simple mathematical rules for probability.
 Be able to perform statistical inference based on data.
 Be able to perform regression analysis to describe the relationship between two variables  including inference related to the parameters in the model.
 Be able to process and analyze data using software as well as simple programming.
Upon completion of the course, students must have developed an understanding of how to arrive at the various methods and procedures used in statistics, as well as being able to apply these efficiently.
 Sequences
 Descriptive statistics
 Probability models
 Probability theori
 Random variables
 Central limit theorem
 Estimation
 Inference
 Regression analysis
To support the course literature, notes and exercises with solutions have been prepared for each topic in the course. The students themselves are responsible for acquiring the material in the syllabus. The exam will be based on the students having followed the lectures and solved the exercises throughout the term. Collective feedback and guidance is provided in the form of solutions and/or review in plenary. The course makes use of mathematical/statistical software for calculations, data handling, and simple programming.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Basic skills in mathematics and statistics equivalent to admission requirements for the program.
Assessments 

Exam category: School Exam Form of assessment: Written School Exam  pen and paper Exam/handin semester: First Semester Weight: 100 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: MET11901 Grading scale: ECTS Resit: Examination every semester 
Activity  Duration  Comment 

Teaching  54 Hour(s)  
Group work / Assignments  96 Hour(s)  
Student's own work with learning resources  40 Hour(s)  
Examination  10 Hour(s)  Exam incl. preparation 
A course of 1 ECTS credit corresponds to a workload of 2630 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.