Statistics and ProbabilityLaajuus (5 cr)
Code: II10017
Credits
5 op
Teaching language
- English
Responsible person
- Ninja Tuupainen
Objective
You are able to solve basic probability and statistical problems. You know basic sampling methods and how to analyse data with them.
Content
Sampling methods
Location measures
- Mean
- Median
- Mode
Statistical dispersion
- Standard deviation
- Range
- Interquartile range
- Variance
Regression and correlation
Probability
Enrollment
01.10.2024 - 31.10.2024
Timing
27.01.2025 - 09.05.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Industrial Management
Campus
Wärtsilä Campus Karjalankatu 3
Teaching languages
- English
Seats
20 - 50
Degree programmes
- Degree Programme in Industrial Management
Teachers
- Aki Summanen
Teacher in charge
Aki Summanen
Groups
-
IINS24Bachelor of Engineering, Industrial Management, Full-time Studies, Fall, 2024
Objective
You are able to solve basic probability and statistical problems. You know basic sampling methods and how to analyse data with them.
Content
Sampling methods
Location measures
- Mean
- Median
- Mode
Statistical dispersion
- Standard deviation
- Range
- Interquartile range
- Variance
Regression and correlation
Probability
Evaluation scale
H-5
Enrollment
01.10.2023 - 31.10.2023
Timing
08.01.2024 - 28.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Industrial Management
Campus
Wärtsilä Campus Karjalankatu 3
Teaching languages
- English
Seats
20 - 50
Degree programmes
- Degree Programme in Industrial Management
Teachers
- Aki Summanen
Teacher in charge
Aki Summanen
Groups
-
IINS23Bachelor of Engineering, Industrial Management, Full-time Studies, Fall, 2023
Objective
You are able to solve basic probability and statistical problems. You know basic sampling methods and how to analyse data with them.
Content
Sampling methods
Location measures
- Mean
- Median
- Mode
Statistical dispersion
- Standard deviation
- Range
- Interquartile range
- Variance
Regression and correlation
Probability
Exam schedules
Midterm exam is on week 9. Final is on week 17.
Final exam on April 2024 according to the study plan.
This course can't be done on EXAM-environment.
Completion alternatives
The course can be done also by attending to final exam on spring 2024.
Student workload
Lessons and exams 36,5 hours. Guided exercises 28 hours.
Self-learning about 98,5 hours
Total 135 hours
One academic credit equals about 27 hours of work for student.
Evaluation scale
H-5
Assessment methods and criteria
Evaluation is based on the exams, specified exercises and assignments announced by the teacher on the lessons and on Moodle. If the student's total amount of points is
- less than 35 % of maximum: grade 0 (fail)
- 35 - 48 % of maximum: grade 1
- 49 - 62 % of maximum: grade 2
- 63 - 76 % of maximum: grade 3
- 77 - 90 % of maximum: grade 4
- more than 90 % of maximum: grade 5.
Assessment criteria, fail (0)
The student has difficulties to read statistical texts and to recognize basic expressions in statistics and probability
There are difficulties to use basic mathematical tools
The student is unable to apply mathematics
Less than 35 % of points from exams and other exercises or assignments
Assessment criteria, satisfactory (1-2)
The student knows basic expressions in statistics and probability and can easily read statistical texts
The student is able to use basic mathematical tools
The student is able to apply basics on easy applications
Grade 1: 35 - 48 % of points from exams and other exercises or assignments
Grade 2: 49 - 62 % of points from exams and other exercises or assignments
Assessment criteria, good (3-4)
The student is able to read statistical texts and uses expressions in statistics and probability
The student is able to use mathematical tools
The student is able to apply mathematics on applications
Grade 3: 63 - 76 % of points from exams and other exercises or assignments
Grade 4: 77 - 90 % of points from exams and other exercises or assignments
Assessment criteria, excellent (5)
The student is able to read fluently statistical texts and uses difficult expressions in statistics and probability
The student is able to use difficult mathematical tools
The student is able to apply mathematics fluently on applications
More than 90 % of points from exams and other exercises or assignments
Enrollment
01.10.2022 - 31.10.2022
Timing
13.02.2023 - 12.05.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Industrial Management
Campus
Wärtsilä Campus Karjalankatu 3
Teaching languages
- English
Degree programmes
- Degree Programme in Industrial Management
Teachers
- Ninja Tuupainen
- Aki Summanen
Teacher in charge
Ninja Tuupainen
Groups
-
IINS22Bachelor of Engineering, Industrial Management, Full-time Studies, Fall, 2022
Objective
You are able to solve basic probability and statistical problems. You know basic sampling methods and how to analyse data with them.
Content
Sampling methods
Location measures
- Mean
- Median
- Mode
Statistical dispersion
- Standard deviation
- Range
- Interquartile range
- Variance
Regression and correlation
Probability
Exam schedules
Midterm exams are agreed on the lessons depending on the progress of the course.
There are 4 mandatory Excel-assingments to return.
Final exam on April 2023 according to the study plan.
It is possible to resit the exam with the same course content on 11.5.2023. The exam resit for the course are announced on Moodle.
This course can't be done on EXAM-environment.
Completion alternatives
The course can be done also by attending to final exam on spring 2023.
Student workload
Lessons and exams 36,5 hours
Self-learning about 98,5 hours
Total 135 hours
One academic credit equals about 27 hours of work for student.
Evaluation scale
H-5
Assessment methods and criteria
Evaluation is based on the exams, specified exercises and assignments announced by the teacher on the lessons and on Moodle. If the student's total amount of points is
- less than 35 % of maximum: grade 0 (fail)
- 35 - 48 % of maximum: grade 1
- 49 - 62 % of maximum: grade 2
- 63 - 76 % of maximum: grade 3
- 77 - 90 % of maximum: grade 4
- more than 90 % of maximum: grade 5.
Assessment criteria, fail (0)
The student has difficulties to read statistical texts and to recognize basic expressions in statistics and probability
There are difficulties to use basic mathematical tools
The student is unable to apply mathematics
Less than 35 % of points from exams and other exercises or assignments
Assessment criteria, satisfactory (1-2)
The student knows basic expressions in statistics and probability and can easily read statistical texts
The student is able to use basic mathematical tools
The student is able to apply basics on easy applications
Grade 1: 35 - 48 % of points from exams and other exercises or assignments
Grade 2: 49 - 62 % of points from exams and other exercises or assignments
Assessment criteria, good (3-4)
The student is able to read statistical texts and uses expressions in statistics and probability
The student is able to use mathematical tools
The student is able to apply mathematics on applications
Grade 3: 63 - 76 % of points from exams and other exercises or assignments
Grade 4: 77 - 90 % of points from exams and other exercises or assignments
Assessment criteria, excellent (5)
The student is able to read fluently statistical texts and uses difficult expressions in statistics and probability
The student is able to use difficult mathematical tools
The student is able to apply mathematics fluently on applications
More than 90 % of points from exams and other exercises or assignments