Statistics and probabilityLaajuus (3 cr)
Code: MAP22MT04
Credits
3 op
Objective
- The student knows basic probability theory and can use different statistical distributions to solve problems
- The student can statistically analyze and present a data material in a relevant way
- The student can apply regression models to different data sets
Content
Statistics
- descriptive statistics
- probability theory
- discrete and continuous distributions
- Excel as a tool for analyzing data
Curve fitting and regression
- matrix calculus and linear algebra
- linear regression - least squares method
- linearization
- mathematical software (Excel, Mathcad or other program) to build regression models
Qualifications
Functions an equations 1,
Geometry and equations,
Functions an equations 2
Derivator and integrals
Assessment criteria, satisfactory (1)
Statistics and computational skills: Know the basics of statistics and can perform simple calculations
Curve fitting and regression: Know the basics of regression analysis
Mathematics software: Can use a software to solve basic problems
Assessment criteria, good (3)
Statistics and calculation skills: Is familiar with statistics and can perform relevant calculations
Curve fitting and regression: Is familiar with the concept of regression and can perform curve fitting of technical models
Mathematics software: Can use a software to solve applied problems
Assessment criteria, excellent (5)
Statistics and computational skills: Is well versed in statistics and can perform more advanced statistical calculations.
Curve fitting and regression: Can independently perform curve fitting and regression models in advanced technical problems
Mathematics software: Can use a software to solve advanced problems in mechanical engineering
Materials
- Booklet containing theory and exercises
- Relevant textbooks are recommended at the start of the course
Enrollment
01.12.2024 - 02.03.2025
Timing
03.03.2025 - 04.05.2025
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Mechanical and Production Engineering
Teachers
- Tom Lillhonga
Teacher in charge
Niklas Kallenberg
Groups
-
BYL23D-VIngenjör (YH), lantmäteriteknik, 2023 dagstudier
-
PRE23D-VIngenjör (YH), produktionsekonomi, 2023 dagstudier
Objective
- The student knows basic probability theory and can use different statistical distributions to solve problems
- The student can statistically analyze and present a data material in a relevant way
- The student can apply regression models to different data sets
Content
Statistics
- descriptive statistics
- probability theory
- discrete and continuous distributions
- Excel as a tool for analyzing data
Curve fitting and regression
- matrix calculus and linear algebra
- linear regression - least squares method
- linearization
- mathematical software (Excel, Mathcad or other program) to build regression models
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Statistics and computational skills: Know the basics of statistics and can perform simple calculations
Curve fitting and regression: Know the basics of regression analysis
Mathematics software: Can use a software to solve basic problems
Assessment criteria, good (3)
Statistics and calculation skills: Is familiar with statistics and can perform relevant calculations
Curve fitting and regression: Is familiar with the concept of regression and can perform curve fitting of technical models
Mathematics software: Can use a software to solve applied problems
Assessment criteria, excellent (5)
Statistics and computational skills: Is well versed in statistics and can perform more advanced statistical calculations.
Curve fitting and regression: Can independently perform curve fitting and regression models in advanced technical problems
Mathematics software: Can use a software to solve advanced problems in mechanical engineering
Qualifications
Functions an equations 1,
Geometry and equations,
Functions an equations 2
Derivator and integrals
Enrollment
15.06.2024 - 22.09.2024
Timing
02.09.2024 - 13.10.2024
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Mechanical and Production Engineering
Teachers
- Tom Lillhonga
Teacher in charge
Kaj Rintanen
Scheduling groups
- MAP23-K (Size: 40. Open UAS: 0.)
- MAP23-D (Size: 40. Open UAS: 0.)
Groups
-
MAP23D-VIngenjör (YH), maskin- och produktionsteknik, 2023 dagstudier
Small groups
- MAP23-K
- MAP23-D
Objective
- The student knows basic probability theory and can use different statistical distributions to solve problems
- The student can statistically analyze and present a data material in a relevant way
- The student can apply regression models to different data sets
Content
Statistics
- descriptive statistics
- probability theory
- discrete and continuous distributions
- Excel as a tool for analyzing data
Curve fitting and regression
- matrix calculus and linear algebra
- linear regression - least squares method
- linearization
- mathematical software (Excel, Mathcad or other program) to build regression models
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Statistics and computational skills: Know the basics of statistics and can perform simple calculations
Curve fitting and regression: Know the basics of regression analysis
Mathematics software: Can use a software to solve basic problems
Assessment criteria, good (3)
Statistics and calculation skills: Is familiar with statistics and can perform relevant calculations
Curve fitting and regression: Is familiar with the concept of regression and can perform curve fitting of technical models
Mathematics software: Can use a software to solve applied problems
Assessment criteria, excellent (5)
Statistics and computational skills: Is well versed in statistics and can perform more advanced statistical calculations.
Curve fitting and regression: Can independently perform curve fitting and regression models in advanced technical problems
Mathematics software: Can use a software to solve advanced problems in mechanical engineering
Qualifications
Functions an equations 1,
Geometry and equations,
Functions an equations 2
Derivator and integrals
Enrollment
03.02.2024 - 03.03.2024
Timing
04.03.2024 - 05.05.2024
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Mechanical and Production Engineering
Teachers
- Tom Lillhonga
Teacher in charge
Tom Lillhonga
Groups
-
PRE22D-VIngenjör (YH), produktionsekonomi, 2022 dagstudier
-
BYL22D-VIngenjör (YH), lantmäteriteknik, 2022 dagstudier
Objective
- The student knows basic probability theory and can use different statistical distributions to solve problems
- The student can statistically analyze and present a data material in a relevant way
- The student can apply regression models to different data sets
Content
Statistics
- descriptive statistics
- probability theory
- discrete and continuous distributions
- Excel as a tool for analyzing data
Curve fitting and regression
- matrix calculus and linear algebra
- linear regression - least squares method
- linearization
- mathematical software (Excel, Mathcad or other program) to build regression models
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Statistics and computational skills: Know the basics of statistics and can perform simple calculations
Curve fitting and regression: Know the basics of regression analysis
Mathematics software: Can use a software to solve basic problems
Assessment criteria, good (3)
Statistics and calculation skills: Is familiar with statistics and can perform relevant calculations
Curve fitting and regression: Is familiar with the concept of regression and can perform curve fitting of technical models
Mathematics software: Can use a software to solve applied problems
Assessment criteria, excellent (5)
Statistics and computational skills: Is well versed in statistics and can perform more advanced statistical calculations.
Curve fitting and regression: Can independently perform curve fitting and regression models in advanced technical problems
Mathematics software: Can use a software to solve advanced problems in mechanical engineering
Qualifications
Functions an equations 1,
Geometry and equations,
Functions an equations 2
Derivator and integrals
Enrollment
15.06.2023 - 03.09.2023
Timing
28.08.2023 - 15.10.2023
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Mechanical and Production Engineering
Teachers
- Tom Lillhonga
Teacher in charge
Kaj Rintanen
Scheduling groups
- MAP22-K (Size: 30. Open UAS: 0.)
- MAP22-D (Size: 30. Open UAS: 0.)
Groups
-
MAP22D-VIngenjör (YH), maskin- och produktionsteknik, 2022 dagstudier
Small groups
- MAP22-K
- MAP22-D
Objective
- The student knows basic probability theory and can use different statistical distributions to solve problems
- The student can statistically analyze and present a data material in a relevant way
- The student can apply regression models to different data sets
Content
Statistics
- descriptive statistics
- probability theory
- discrete and continuous distributions
- Excel as a tool for analyzing data
Curve fitting and regression
- matrix calculus and linear algebra
- linear regression - least squares method
- linearization
- mathematical software (Excel, Mathcad or other program) to build regression models
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Statistics and computational skills: Know the basics of statistics and can perform simple calculations
Curve fitting and regression: Know the basics of regression analysis
Mathematics software: Can use a software to solve basic problems
Assessment criteria, good (3)
Statistics and calculation skills: Is familiar with statistics and can perform relevant calculations
Curve fitting and regression: Is familiar with the concept of regression and can perform curve fitting of technical models
Mathematics software: Can use a software to solve applied problems
Assessment criteria, excellent (5)
Statistics and computational skills: Is well versed in statistics and can perform more advanced statistical calculations.
Curve fitting and regression: Can independently perform curve fitting and regression models in advanced technical problems
Mathematics software: Can use a software to solve advanced problems in mechanical engineering
Qualifications
Functions an equations 1,
Geometry and equations,
Functions an equations 2
Derivator and integrals
Enrollment
01.12.2022 - 03.03.2023
Timing
04.03.2023 - 01.05.2023
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- Svenska
Degree programmes
- Degree Programme in Mechanical and Production Engineering
Teachers
- Tom Lillhonga
Teacher in charge
Roger Nylund
Groups
-
PRE21D-VIngenjör (YH), produktionsekonomi, 2021 dagstudier
Objective
- The student knows basic probability theory and can use different statistical distributions to solve problems
- The student can statistically analyze and present a data material in a relevant way
- The student can apply regression models to different data sets
Content
Statistics
- descriptive statistics
- probability theory
- discrete and continuous distributions
- Excel as a tool for analyzing data
Curve fitting and regression
- matrix calculus and linear algebra
- linear regression - least squares method
- linearization
- mathematical software (Excel, Mathcad or other program) to build regression models
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Statistics and computational skills: Know the basics of statistics and can perform simple calculations
Curve fitting and regression: Know the basics of regression analysis
Mathematics software: Can use a software to solve basic problems
Assessment criteria, good (3)
Statistics and calculation skills: Is familiar with statistics and can perform relevant calculations
Curve fitting and regression: Is familiar with the concept of regression and can perform curve fitting of technical models
Mathematics software: Can use a software to solve applied problems
Assessment criteria, excellent (5)
Statistics and computational skills: Is well versed in statistics and can perform more advanced statistical calculations.
Curve fitting and regression: Can independently perform curve fitting and regression models in advanced technical problems
Mathematics software: Can use a software to solve advanced problems in mechanical engineering
Qualifications
Functions an equations 1,
Geometry and equations,
Functions an equations 2
Derivator and integrals