Course - Statistics and Sensory Methods - MATV2002
MATV2002 - Statistics and Sensory Methods
About
Examination arrangement
Examination arrangement: School exam
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| School exam | 100/100 | 4 hours | D |
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)
Recommended previous knowledge
Knowledge in mathematics.
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 | To |
|---|---|---|---|
| TMAT2004 | 5.0 | AUTUMN 2020 | |
| TMAT1012 | 2.5 | AUTUMN 2020 |
No
Version: 1
Credits:
7.5 SP
Study level: Foundation courses, level I
Term no.: 1
Teaching semester: SPRING 2024
Language of instruction: Norwegian
Location: Trondheim
- Food Subjects
- Natural Sciences
- Statistics
Department with academic responsibility
Department of Biotechnology and Food Science
Examination
Examination arrangement: School exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD School exam 100/100 D 2024-05-14 15:00 INSPERA
-
Room Building Number of candidates SL110 lilla sone Sluppenvegen 14 35 - Summer UTS School exam 100/100 D 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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"