course-details-portlet

MET2010 - Applied Statistics

About

Examination arrangement

Examination arrangement: Assignment
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Assignment 100/100

Course content

The course gives a practical overview of all the steps in a high quality statistical data analysis using modern computational tools. The course has a special emphasis on using simulation to understand and derive statistical quantities.

Topics covered in the course includes:

  • Finding, importing and cleaning data
  • Transforming, manipulating and combining datasets
  • Visualizing data and statistical relationships
  • Estimating standard errors and confidence intervals for various statistics
  • Repetition of simple linear regression and the least squares method
  • Multiple linear regression models
  • Estimating standard errors and confidence intervals for regression coefficients
  • Model evaluation techniques
  • Nonlinear regression: Quadratic and log-linear models
  • Introduction to Bayesian theory and methods
  • Logistic regression and GLM
  • Time series models
  • Identification and causal modelling
  • Training in the use of Python and associated packages

Learning outcome

Knowledge

The course should provide the student with knowledge of some of the most commonly used types of statistical tests, basic knowledge of multiple regression analysis, and other components of a data analysis.

Skills

The student should be able to perform several types of statistical techniques and to use computer tools to analyze data using descriptive statistics, visualizations and multiple regression analysis.

General competence

The student should gain an understanding of all the steps involved in a high quality data analysis. The student should have a thorough understanding of the general principles for performing statistical tests and good knowledge about the possibilities and limitations of using multiple regression models.

Learning methods and activities

Digital and in-person lectures, labs and exercises.

Compulsory assignments

  • Obligatorisk skriftlig innlevering

Further on evaluation

Mandatory assignments must be passed to gain entry to the exam. This is a group project assignment with up to 3 students per group. Students can hand in individually or in a group of 2 if they wish.

Mandatory assignment will be specified at semester start.

Specific conditions

Required previous knowledge

None.

Course materials

The final syllabus is announced at the beginning of the semester.

Credit reductions

Course code Reduction From To
ME220 6.0 SPRING 2005
ME220 6.0 SPRING 2005
ME210 6.0 SPRING 2005
SØK1005 4.0 AUTUMN 2023
Facts

Version: A
Credits:  7.5 SP
Study level: Intermediate course, level II

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2023

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Statistics
  • Economics and Administration
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
NTNU Business School

Examination

Examination arrangement: Assignment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Assignment 100/100

Release
2023-12-08

Submission
2023-12-15


12:00


12:00

INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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