Applied Statistics

TECH3 Applied Statistics

Autumn 2024

Spring 2024
  • Topics

    TECH3 Applied Statistics is taught in the second semester, focusing on the fundamental principles of statistics and probability. We cover topics such as data summarization, probability distributions, estimation, hypothesis testing, and relationship measurement. The course utilizes simulations, resampling techniques, and Python programming to handle uncertainty in estimators and decisions. 

    The course consists of five modules:

    1. Summarizing and visualizing Data
      • Focus: Making the data more accessible.
      • Key topics: Central statistics summarizing data, histograms, scatterplots, boxplots, human perceptional limitations.
      • Special Emphasis: What is statistical thinking? Never create a pie chart. 
    2. Probability, random variables, probability distributions and simulations
      • Focus: Random variables, distributions and the link to simulations.
      • Key topics: Probability distributions, conditional probability, independence, the law of large numbers, pseudo-random numbers.
      • Special Emphasis: Estimating the probability of complex outcomes through simulations. 
    3. Estimation, sampling distributions and resampling
      • Focus: Modern inference using Monte Carlo methods. 
      • Key topics: Central limit theorem, sampling error of various statistics, Monte Carlo simulation.
      • Special Emphasis: Distinguish between a population and a sample and between population parameters and sample statistics.
    4. Designing studies, hypothesis testing, and quantifying effects
      • Focus: Modern hypothesis testing using bootstrap methods.
      • Key topics: classical test statistics, confidence intervals, effect size, power analysis, AB-testing.  
      • Special Emphasis: Statistical significance versus practical significance. Describe the proper interpretations of a p-value and common misinterpretations.
    5. Measuring relationships and fitting models
      • Focus: The difference between modelling continuous and categorical relationships.
      • Key topics: Correlation, causation, linear regression, predictions, odds ratio, binary regression, chi-squared tests.  
      • Special Emphasis: Address overfitting using cross-validation.

    TECH3 builds upon the skills acquired from TECH2, providing students with statistical knowledge and skills essential for addressing practical business issues. It specifically focuses on challenges related to understanding and meeting the needs of consumers and users, emphasizing sustainability and aligning with topics covered in courses like EBO1, MAB1, and MAB2.  TECH3 is also a prerequisite for TECH5, where more advanced statistical analysis techniques are explored.

  • Learning outcome

    Upon completing the course, the students can:


    • Understand basic statistical theory and corresponding methods,  and how to apply this knowledge in practical situations.


    • Explore data using software that can summarize and visualize data.
    • Master basic probability theory.
    • Make inferences about an entire population based on a sample of individuals from that population using both classical statistical methods and modern resampling techniques.
    • Design basic experiments, perform hypothesis testing, and quantify effects.
    • Measure relationships between both categorical and continuous variables. 
    • Fit and evaluate regression models for both inference and prediction.

    General Competence

    • Identify and solve statistical problems.
    • Perform basic data analysis using modern computer tools.
    • Perform data-driven decision-making for a sustainable future.

  • Teaching

    Teaching consists of interactive sessions and lectures given at campus. Parts of the curriculum will be supported by online based modules containing short videos, exercises and notes. We organize collaborative learning sessions at campus, with mini cases and on-site feedback from lecturers. The cases will be formulated in collaboration with EBO1, MAB1 and MAB2 lecturers.

  • Recommended prerequisites

    This course presumes mathematical and Python programming skills equivalent to TECH1 and TECH2.

  • Credit reduction due to overlap


  • Compulsory Activity

    Approved hand-in assignments.

  • Assessment

    Individual school exam (4 hours - pen and paper). The exam must be answered in English.

  • Grading Scale


  • Computer tools


  • Literature

    The course curriculum will be available in Leganto.

  • Permitted Support Material


    One bilingual dictionary (Category I) 

    All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support material and 


ECTS Credits
Teaching language

Spring. Will be offered Spring 2025.

Course responsible

Assistant Professor Sondre Nedreås Hølleland, Department of Business and Management Science (Main course responsible)

Associate Professor Geir Drage Berentsen, Department of Business and Management Science