Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
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COLLEGE OF ARTS AND SCIENCES
Second Semester, Academic Year 2020-2021
SYLLABUS in Math 2 – Multivariate Statistics
Institutional Vision, Mission, and Goals
Vision:
An innovative and technologically-advanced State College in Caraga.
Mission:
To provide relevant, high quality and sustainable instruction, research, production and extension programs and
services within a culture of credible and responsive institutional governance.
Institutional Intended Learning Outcomes
Goals:
1. Foster application of the discipline and provide its learner with industry-based training and education
particularly in engineering, technology and fisheries.
2. Conduct and utilize studies for the development of new products, systems and services relevant to Philippine
life and of the global village.
3. Promote transfer of technology and spread useful technical skills, thus empowering its learners and their
activities.
: SSCT graduates are expected to:
1. Demonstrate globally competitive skills;
2. Manifest positive work ethics and flexibility in various work condition;
3. Exhibit knowledge deemed essential towards work requirements.
Program Goals
The BSES program aims to expose students in an integrated way to environmental processes and phenomena, as well
as environmental issues, from the perspective of the natural sciences. It aims to train them to recognize and understand
the natural environment, how humans affect the environment, and how the environment impacts on society.
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
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Program Educational Objectives
The BS in Environmental Science program aims:
1. To equip students with sufficient knowledge on the scientific theories and techniques needed to monitor and
understand environmental quality;
2. To enable students to integrate and apply the various disciplines towards the understanding of environmental
problems;
3. To make the students knowledgeable regarding relevant local, regional and global environmental issues; and
4. To enable students to employ a rational structured approach to solving environmental problems.
Program Outcome(s)
Upon the completion of the BSES program, the students must be able to:
(a) demonstrate broad and coherent knowledge and understanding in the core areas of environmental science;
(b) disseminate effectively knowledge pertaining to sound environmental protection, conservation, utilization and
management;
(c) analyze local environmental issues and problems in the regional and global context;
(d) apply appropriate knowledge and innovation related to the environment;
demonstrate the ability to contribute to the protection and management of the environment;
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SURIGAO STATE COLLEGE
OF TECHNOLOGY
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Course Code
Course Descriptive Title
Course Credit
Pre-requisites/Co-requisites
Math 2
Multivariate Statistics
3 units
Parametric and Non-Parametric Statistics for Sciences
Course Description
This course deals with applications of multivariate statistics in analyzing data from environmental sciences. It provides
students competencies on the overview of multivariate methods, multivariate analysis of variance (MANOVA), multivariate
analysis of covariance (MANCOVA), multiple linear regression (MLR), exploratory factor analysis (EFA), cluster analysis
(CA), canonical correlation analysis, and canonical correspondence analysis,
At the end of the course, the students should be able to:
Course Intended Learning Outcomes
1.
2.
3.
4.
5.
6.
At the end of the semester, the students are expected to:
explain multivariate statistics and its application to environmental sciences;
relate measurement scales to multivariate techniques;
differentiate statistical significance and statistical power;
differentiate the appropriateness of various multivariate techniques;
discuss the assumptions of multivariate techniques;
interpret outputs of different multivariate statistical procedures from statistical softwares.
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SURIGAO STATE COLLEGE
OF TECHNOLOGY
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Detailed Course Syllabus
Intended
Learning
Outcome
Express
understanding of
the Vision and
Mission
statements of
SSCT including its
Goals and
Objectives.
Scrutinize the
syllabus by looking
into the ILOs,
Subject Matter,
TLAs, Assessment
Strategies, Values
and References.
Design strategies
that will help meet
the requirements
and obtain desired
grades/marks for
the course.
Topics
ORIENTATION ON THE
COURSE VMGO/ SYLLABUS
VMGO
SYLLABUS
GRADING SYSTEM
Time
Frame
1 hour
Teaching and Learning
Activities
Big Group Discussion on
VMGO
Documentary Analysis of
Syllabus and Grading
System
Concept Mapping
(Sunflower
Map/Fishbone Map) on
strategies to meet course
requirements
Assessmen
t Tasks
Resources
Values
Integration
Obedience,
Punctuality,
Diligence
References
Student
Handbook
Syllabus
Remarks
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
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Overview of Multivariate
Methods
 explain
multivariate
statistics and its
application
to
environmental
sciences;

Multivariate Analysis in
Statistical Terms
 relate
measurement
scales
multivariate
techniques;

Some Basic Concepts of
Multivariate Analysis
 differentiate
statistical
significance
and
statistical power;

Statistical Significance
versus Statistical Power
 differentiate
the
appropriateness of
various
multivariate
techniques;

Classification of
Multivariate Techniques
6 hours




Modular Instruction
 Restricted
Essay
Lecture
Concept Mapping
Google
Class
Discussions
 Messenger
Chat
Discussions
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 SelfDiscipline
 Prudence
 Diligence
6 hours
 Restricted
 Modular Instruction
Essay
 Lecture

Document
 Analysis of Research
Analysis
Articles
 Google
Class
Discussions
 Messenger
Chat
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
 SelfDiscipline
 Prudence
 Diligence
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Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
to
 discuss
the
assumptions
of
MANOVA;
 interpret
software outputs of
MANOVA;
Multivariate Analysis of
Variance (MANOVA)
 Assumptions
 Interpretation
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
Revision No.
Effective Date
Page No.
Discussions
 discuss
the
assumptions
of
MANCOVA;
 interpret
software outputs of
MANCOVA;
Multivariate Analysis of
Covariance (MANCOVA)


Assumptions
Interpretation
6 hours





 discuss
the
assumptions
of
MLR;
 interpret
software outputs of
MLR;
Multiple Linear Regression
(MLR)
 Assumptions
 Interpretation
Midterm Examinations
6 hours
2 hours
 Restricted
Modular Instruction
Essay
Lecture
 Document
Analysis
Analysis of Research
Articles
Google
Class
Discussions
Messenger
Chat
Discussions
 Restricted
 Modular Instruction
Essay
 Lecture

Document
 Analysis of Research
Analysis
Articles
 Google
Class
Discussions
 Messenger
Chat
Discussions
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
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 SelfDiscipline
 Prudence
 Diligence
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
 SelfDiscipline
 Prudence
 Diligence
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
Revision No.
Effective Date
Page No.
 discuss
the
assumptions
of
CA;
 interpret
software outputs of
CA;
 discuss
the
assumptions
of
EFA;
 interpret
software outputs of
EFA;
 discuss
assumptions
CCrA;
 interpret
the
of
Cluster Analysis (CA)
 Assumptions
 Interpretation
Exploratory Factor Analysis
(EFA)


6 hours
6 hours
Assumptions
Interpretation
Canonical Correlation
Analysis (CCrA)
 Assumptions
 Interpretation
6 hours
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 Modular Instruction
 Multiple
Choice
 Lecture
Test
 Analysis of Research
Articles
 Google
Class
 Restricted
Discussions
Essay
 Messenger
Chat

Document
Discussions
Analysis
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 SelfDiscipline
 Prudence
 Diligence
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
 Restricted
 Modular Instruction
Essay
 Lecture

Document
 Analysis of Research
Analysis
Articles
 Google
Class
Discussions
 Messenger
Chat
Discussions
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 SelfDiscipline
 Prudence
 Diligence
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
 SelfDiscipline
 Prudence
 Diligence
 Restricted
 Modular Instruction
Essay
 Lecture
 Analysis of Research  Document
Analysis
Articles
 Google
Class
Discussions
 Messenger
Chat
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
Revision No.
Effective Date
Page No.
software outputs of
CCrA;
 discuss
the
assumptions
of
CCdA;
 interpret
software outputs of
CCrdA;
Discussions
Canonical Correspondence
Analysis (CCdA)
 Assumptions
 Interpretation
6 hours
Final Examinations
2 hours
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Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 Modular Instruction
 Restricted
Essay
 Lecture
 Analysis of Research  Document
Analysis
Articles
 Google
Class
Discussions
 Messenger
Chat
Discussions
 Multiple
Choice
Test
 Learning
Module
 Discussion
Videos
 Research
Articles
 SPSS
Outputs
 Google Suite
 Facebook
Messenger
Group Chat
 SelfDiscipline
 Prudence
 Diligence
Hair,
Joseph
Jr. F. (2019).
Multivariate
Data Analysis.
Cengage
Learning EMEA.
United Kingdom
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
Revision No.
Effective Date
Page No.
FM-SSCT-ACAD-002
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20 September 2018
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Course Requirements:





Facebook Messenger Group Chat Interaction
Google Meeting Attendance
Module Posttests
Project
Midterm & Final Examination
Grading System:
Criteria
 Quizzes and Online Outputs/Interaction
 Performance Tasks (Project)
 Major Examination
TOTAL
Grade
25%
35%
40%
100%
Grade Point
1.0
1.5 – 1.1
2.0 – 1.6
2.5 – 2.1
2.9 – 2.6
3.0
5.0
DRP
INC
Description
Excellent
Very Good
Highly Satisfactory
Good
Satisfactory
Passing
Failed due to poor performance, absences, withdrawal without notice
Dropped with approved dropping slip
Incomplete requirements but w/ passing class standing. INC is for non-graduating
students only
NG
No Grade
Source: SSCT Student Handbook
Course Policies:
1. Enrolment to Google Class and Facebook Messenger Group Chat is required.
2. Attendance to Google Meetings and participation in Group Chats are also required.
3. It is a part of your education to learn responsibility and self-discipline, particularly with regard to academic honesty. Cheating is defined to include an attempt to defraud,
deceive, or mislead the instructor in arriving at honest grade assessment. Plagiarism is a form of cheating that involves presenting as one’s own work the ideas or work of
Document Code No.
SURIGAO STATE COLLEGE
OF TECHNOLOGY
Revision No.
Effective Date
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FM-SSCT-ACAD-002
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20 September 2018
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another. Online outputs which are exactly the same will not be considered cheating. Therefore, all portions of any test, project, or major examination submitted by you for
a grade must be your own work, unless you are instructed to work collaboratively. Cheating in a major course examination by a student will entail a failing mark for the
given course. Plagiarism in papers and other works will entail zero score for the said requirement.
4. You must regularly check the Google Classroom and Group Chat for updates.
5. Enough time will be given to do your tasks in the course. You should submit your outputs on or before the deadline.
6. All tasks will be posted and outputs will be submitted in Google Classroom. Outputs submitted through the Facebook Messenger will not be counted except on meritorious
cases (but highly discouraged).
7. This class policy serves as our written agreement for the whole semester.
Date Revised: January 25, 2021
Effectivity: February 01, 2021
Prepared by:
Checked and Reviewed by:
RUEL T. BUBA
Assistant Professor II
GHELEENE S. BUENAFLOR
Program Chair
Date: _____________________
Date: _____________________
Noted by:
Recommended by:
Approved by:
LOUIDA P. PATAC, PhD
Dean
RONITA E. TALINGTING, PhD
Campus Director
EMMYLOU A. BORJA, EdD
VP for Academic Affairs
Date: _______________
Date: __________________
Date: ________________