MET 3431 Statistics

MET 3431 Statistics

Course code: 
MET 3431
Department: 
Economics
Credits: 
7.5
Course coordinator: 
Njål Foldnes
Product category: 
Bachelor
Portfolio: 
Bachelor - Core Courses
Semester: 
2019 Spring
Active status: 
Active
Teaching language: 
Norwegian
Course type: 
One semester
Introduction

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. 

Learning outcomes - Knowledge

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. 

Learning outcomes - Skills

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. 

Learning Outcome - Reflection

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.

Course content
  • Collection of data
  • Describing the sample at hand
  • Probability
  • Confidence intervals for mean and proportion 
  • Hypothesis tests  for mean and proportion 
  • Correlation and regression
  • Chi-square test
Learning process and requirements to students

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.

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.

Software tools
SAS - JMP
Additional information

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.

Qualifications

Higher Education Entrance Qualification.

Required prerequisite knowledge

No specific prerequisites required.

Mandatory courseworkCourseworks givenCourseworks requiredComment coursework
Mandatory85In 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.
Mandatory coursework:
Mandatory coursework:Mandatory
Courseworks given:8
Courseworks required:5
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 categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
MET34311
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination every semester
100Yes5 Hour(s)
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
Duration:5 Hour(s)
Comment:
Exam code:MET34311
Grading scale:ECTS
Resit:Examination every semester
Exam organisation: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Teaching on Campus
48 Hour(s)
Group work / Assignments
50 Hour(s)
Prepare for teaching
42 Hour(s)
Working with SAS JMP (or some statistical software)
Student's own work with learning resources
40 Hour(s)
Examination
20 Hour(s)
Exam incl. preparations.
Sum workload: 
200

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.