Module Handbook-Mathematics-Universitas Brawijaya
Module Handbook
Module Name: Introduction to Linear Regression
Module Level: Bachelor
Abbreviation, if Applicable:
MAM62402 Sub-Heading, if
Applicable:
- Courses included in the module, if applicable
Introduction to Linear Regression Semester/term: 2nd/1st year
Module Coordinator(s): Chair of the Lab. Industrial and Finance Mathematics Lecturer(s) Prof. Dr. Agus Widodo, M.Kes.
Drs. Imam Nurhadi Purwanto, M.T.
Kwardiniya Andawaningtyas, S.Si., M.Si.
Language Bahasa Indonesian
Classification within the curriculum
Elective Studies Teaching format / class
hours per week during semester:
100 minutes lectures per week
Workload: Total workload is 3 ECTS, which consists of 1.67 hours lectures, 2 hours structured activities, 2 hours independent learning, 16 week per semester, and a total 90.67 hours per semester including mid exam and final exam.
Credit Points: 2
Requirements according to the examination regulations:
Students have taken Introduction to Linear Regression course (MAM62402), have attendance at least 80%, and have the examination card when where the course is stated on.
Recommended prerequisties
Students have taken Introduction to Statistics course (MAM61301) and have participated in the final exam on the module.
Learning
goals/competencies or
Module
objectives/intended learning outcomes
After completing this course the student should have
CLO 1 : ability to understand the basic concepts of linear regression.
CLO 2 : ability to understand and calculate simple linear regression.
CLO 3 : ability to understand and calculate multiple linear regression.
CLO 4 : ability to understand and test the assumptions of linear regression.
CLO 5 : ability to determine the best linear regression model and apply the results.
CLO 6 : ability to practice a simple example based on the objectives and characteristics of linear regression, determine the best linear regression model and interpret the results.
Content: Topics:
1. The basic concept of linear regression : the definition of linear regression, the difference between linear and nonlinear
Module Handbook-Mathematics-Universitas Brawijaya regression, the definition of simple linear regression and multiple linear regression.
2. Simple Linear Regression: Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE).
3. Multiple Linear Regression: Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE).
4. Test of linear regression assumptions: linearity assumptions, residual normality assumptions, non-autocorrelation assumptions and non-multicollinearity assumptions.
5. Determination of the best linear regression model: AIC, SIC and Mallow’s Cp.
6. Application of the best linear regression model in the industrial field.
Study / exam achievements:
The final mark will be weighted as follows:
No. Assessment methods (component, activities). Weight 1. Activity 20 % 2. Mid examination 35 % 3. Final examination 45 % Final grades is defined as follow:
A : 80 < Final Mark ≤ 100 B+ : 75 < Final Mark ≤ 80 B : 69 < Final Mark ≤ 75 C+ : 60 < Final Mark ≤ 69 C : 55 < Final Mark ≤ 60 D+ : 50 < Final Mark ≤ 55 D : 44 < Final Mark ≤ 50 E : 0 ≤ Final Mark ≤ 44
Forms of Media Slides and LCD projectors, laptop/ computer, whiteboards.
Learning Methods Lecture
Literature 1. Chatterjee, S and Simonoff, J.S., 2013, Handbook of Regression Analysis, Willey, New York.
2. Montgomery, D.C., 1992, Introduction to Linear Regression Analysis Willey, New York.
3. Sembiring , R.K., 1995, Analisis Regresi, Penerbit ITB, Bandung.
4. Sumarto, S.Y., 1988, Analisis Regresi dan Korelasi: Teori dan Terapannya , Universitas Brawijaya, Malang
Notes:
