course-details-portlet

MATV2002

Statistics and Sensory Methods

Choose study year

Assessments and mandatory activities may be changed until September 20th.

Credits 7.5
Level Foundation courses, level I
Course start Spring 2026
Duration 1 semester
Language of instruction Norwegian
Location Trondheim
Examination arrangement School exam

About

About the course

Course content

- Basic terms and sizes: population and range, average, median, variance, standard deviation - Graphic representations and presentation of statistical data - Probability calculation: quantitative, conditional probability, independence and combinatorics - Stochastic models notation, calculation of probability, expectation and variance of discrete and continuous models - Probability Distributions: Theory and Practical Use of the Binomic and Hypergeometric Model, Poisson Distribution, Normal Distribution and Central Limit Theorem - Statistical methods: determination of point estimators and confidence intervals and implementation of hypothesis testing with p-values in known models - Comparison of groups with t-tests with formulas and on PC - Implementation of Chi-squared test and variance analysis on PC - Analysis of correlation and linear regression - The sensory senses - Sensory science as an analytical method (from planning to final results) - Sensory methods with emphasis on discrimination tests

Learning outcome

After completing the course, the candidate is expected to have the following learning outcomes regarding:

KNOWLEDGE: The candidate has knowledge of basic concepts and theory in probability calculations and statistical inference (K1). The candidate can calculate statistical goals for sample data and produce results in tables and with graphics (K2). The candidate can use linear regression and evaluate the results of the regression analysis (K3). The candidate can account for common probability models and calculate probability of events (K4). The candidate can calculate confidence intervals and perform hypothesis tests on the basis of collected data (K5). The candidate can describe the senses as a sensory instrument and know what to emphasize as an assessor and as a panel leader when performing a sensory test (K6). The candidate has knowledge of which sensory issues can be solved using the discrimination tests triangular test, paired comparison test and ranking test (K7). The candidate can describe practical implementation and outcome management for the discrimination tests triangular test, paired comparison test and ranking test (K8).

SKILLS: The candidate is able to interpret the results of studies presented with confidence intervals and p-values from hypothesis tests (F1). The candidate can use computer programs for statistical calculations and analyzes (F2). The candidate is able to participate as an assessor in a semi-trained sensory panel (F3).

GENERAL COMPETENCE: The candidate has basic knowledge to plan and execute a project work that involves sensory analysis where statistical understanding is also important (G1).

Learning methods and activities

Lectures (60 h), guided theoretical exercises (34 h) compulsory laboratory exercises (6 h) and self-study (105 h).

Compulsory assignments

  • Theory exercises
  • Lab exercises

Further on evaluation

Number of compulsory written exercises is 5, the number of compulsory lab exercises is 3, both exercises and lab must be approved in its entirety to gain admission to the exam. The exercises are submitted in writing in groups. Formula booklet and sensory tables are attached to the exam set. If compulsory work requirements have been passed, the candidate do not have to do the requirements over again at new/postponed exam.

New/postponed exam: August

In the case of applications for crediting, approving and filing of subjects from previous cohorts or other institutions' corresponding education programs, each application will be processed individually and the applicant must be able to include credits reduction on overlapping subjects.

Specific conditions

Admission to a programme of study is required:
Food Science, Technology and Sustainability (MTMAT)

Required previous knowledge

Study rights requirements. The course is reserved for students with a Bachelor's degree in Food Science, Technology and Sustainability , NTNU, Trondheim. If capacity, other students may apply to take the course.

Course materials

Statistikk for universiteter og høgskoler, Gunnar G. Løvås

Handed out material (Blackboard) in the sensory part of the course.

Credit reductions

Course code Reduction From
TMAT2004 5 sp Autumn 2020
TMAT1012 2.5 sp Autumn 2020
This course has academic overlap with the courses in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Food Subjects
  • Natural Sciences
  • Statistics

Contact information

Course coordinator

Lecturers

Department with academic responsibility

Department of Biotechnology and Food Science

Examination

Examination

Examination arrangement: School exam
Grade: Letter grades

Ordinary examination - Spring 2026

School exam
Weighting 100/100 Examination aids Code D Duration 4 hours Exam system Inspera Assessment Place and room Not specified yet.

Re-sit examination - Summer 2026

School exam
Weighting 100/100 Examination aids Code D Duration 4 hours Exam system Inspera Assessment Place and room Not specified yet.