HOLY ANGEL UNIVERSITY College of Engineering & Architecture
Department of Computer Engineering
University Vision, Mission, Goals and Objectives:
Mission Statement (VMG)
We, the academic community of Holy Angel University, declare ourselves to be a Catholic University. We dedicate ourselves to our core purpose, which is to provide accessible quality education that transforms students into persons of conscience, competence, and compassion. We commit ourselves to our vision of the University as a role-model catalyst for countryside development and one of the most influential, best managed Catholic universities in the Asia-Pacific region. We will be guided by our core values of Christ-centeredness, integrity, excellence, community, and societal responsibility. All these we shall do for the greater glory of God. LAUS DEO SEMPER!
College Vision, Goals and Objectives:
Vision
A center of excellence in engineering and architecture education imbued with Catholic mission and identity serving as a role-model catalyst for countryside development
Mission
To provide accessible quality engineering and architecture education leading to the development of conscientious, competent and compassionate professionals who continually contribute to the advancement of technology, preserve the environment, and improve life for countryside development.
Goals
The College of Engineering and Architecture is known for its curricular programs and services, research undertakings, and community involvement that are geared to produce competitive graduates:
- who are equipped with high impact educational practices for global employability and technopreneurial opportunities;
- whose performance in national licensure examinations and certifications is consistently above national passing rates and that falls within the 75th to 90th percentile ranks; and,
- who qualify for international licensure examinations, certifications, and professional recognitions;
Objectives
In its pursuit for academic excellence and to become an authentic instrument for countryside development, the College of Engineering and Architecture aims to achieve the following objectives:
1. To provide students with fundamental knowledge and skills in the technical and social disciplines so that they may develop a sound perspective or competent engineering and architecture practice;
2. To inculcate in the students the values and discipline necessary in developing them into socially responsible and globally competitive professionals;
3. To instill in the students a sense of social commitment through involvement in meaningful community projects and services;
4. To promote the development of a sustainable environment and the improvement of the quality of life by designing technology solutions beneficial to a dynamic world;
5. To adopt a faculty development program that is responsive to the continuing development and engagement of faculty in research, technopreneurship, community service and professional development activities both in the local and international context;
6. To implement a facility development program that promotes a continuing acquisition of state of the art facilities that are at par with leading engineering and architecture schools in the Asia Pacific region; and,
7. To sustain a strong partnership and linkage with institutions
Relationship of the Program Educational Objectives to the Vision-Mission of the University and the College of Engineering & Architecture:
Computer Engineering Program Educational Outcomes (PEOs):
Within a few years after graduation, our graduates of the Computer Engineering program are expected to have:
Vision-Mission
Christ-
Centeredness Integrity Excellence Community Societal
Responsibility
1. Practiced their profession √ √ √ √ √
2. Shown a commitment to life-long learning √ √ √ √ √
3. Manifested faithful stewardship √ √ √ √ √
Relationship of the Computer Engineering Program Outcomes to the Program Educational Objectives:
Computer Engineering Student Outcomes (SOs):
At the time of graduation, BS Computer Engineering program graduates should be able to:
PEOs
1 2 3
a) Apply knowledge of mathematics, physical sciences, and engineering sciences to the practice of Computer
Engineering. √ √ √
b) Ability to design and conduct experiments, as well as to analyze and interpret data √ √ √
c) Ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability, in accordance with standards
√ √ √
d) Ability to function in multidisciplinary teams √ √ √
e) An ability to identify, formulate and solve engineering problems √ √ √
f) An understanding of professional and ethical responsibility √ √ √
g) An Angelite is able to demonstrate and master the ability to listen, comprehend, speak, write and convey ideas
clearly and effectively, in person and through electronic media to all audiences. √ √ √
h) Broad education necessary to understand the impact of engineering solutions in a global, economic, environmental,
and societal context √ √ √
i) Recognition of the need for, and an ability to engage in life-long learning and to keep current of the development in
the field √ √ √
j) Knowledge of contemporary issues
√ √ √
k) Ability to use the techniques, skills, and modern engineering tools necessary for computer engineering practice.
√ √ √
l) Knowledge and understanding of engineering and management principles as a member and leader in a team, to
manage projects and in multidisciplinary environments. √ √ √
COURSE SYLLABUS
Course Title PROBABILITY AND STATISTICS Course Code ADVMATH
Course Credit Three (3) units Year Level Third (3rd)
Pre-requisite: ALGEBRA 2 Course Calendar 1st Semester
Course Description:
The course covers the basic principles of statistics; presentation and analysis of data; averages, median, mode; deviations; probability distributions;
normal curves and applications; regression analysis and correlation; application to engineering problems Course Outcomes (COs):
After completing this course, the students will be able to:
Relationship to the Student Outcomes:
a b C d e f g h i j k l
1. Develop familiarity and the skill on how to apply different concepts and different methods for sampling, presentation and interpretation of data.
E E E E
2. Develop students’ understanding of the different concepts of
probability. E E E E
3. Develop students’ skill on constructing hypothesis and conclusion
based on the interpretation and presentation of data E E E E
COURSE ORGANIZATION
Time
Frame Hours CO Code
Link Course Outline Teaching and
Learning Activities Assessment Tools Resources Week
1
3
CO 1
1. Basic Concepts
1) Definition of Statistical Terms 2) Importance of Statistics
3) Difference between Descriptive and Inferential Statistics
2. Steps in Conducting a Statistical Inquiry
Class discussion and
Lecture Examination
(Written)
All References
Week
2 3
CO 1
3. Statistical Presentation of Data 1) Frequency Distribution 2) Graphical Description of Data Textual
Class discussion, Problem Solving
and Lecture Problem Set Examination
(Written) Recitation/Board
Work Assignment
All References
Week
3 3
CO 1
4. Sampling Techniques
Class discussion and Lecture
Examination (Written) Recitation
All References
Week
4-5 6
CO 1
5. Numerical Descriptive Measures 1) Measures of Location in Data Sets:
Mean, Median, Mode, Percentiles, Deciles, and Quartiles for Ungrouped and Grouped Data Skewness and Kurtosis
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written)
All References
Week
6 3
CO 1
2) Measures of Variation:
Range, Mean Absolute Deviation, Variance, Standard Deviation and coefficient of variance for Ungrouped and Grouped Data.
Class discussion, Problem Solving and
Lecture Problem Set
Examination (Written) Recitation/Board
Work
All References
Preliminary Examination
Week
7 3
CO 2
6. Probability Distributions 1) Counting Techniques 2) Probability
3) Mathematical Expectations
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written) Recitation/Board
Work
All References
Week 8-9
6
CO 2
7. Counting Sample Points 8. Probability of an Event
1) Additive Rules
2) Conditional Probability 3) Multiplicative Rules 4) Bayes’ Rule
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written)
All References
Week
10-11 6
CO 2
9. Random Variables and Probability Distribution 1) Discrete Probability Distribution
2) Continuous Probability Distribution 10. Some Discrete Probability Distributions
1) Binomial and Multinomial Distributions 2) Hyper geometric Distribution
Poisson Distribution
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written)
All References
Week
12 3
CO 2
11. Continuous Probability Distribution 1) Normal Distribution
2) Areas under the Normal Curve 3) Application of the Normal Distribution
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written)
All References
Midterm Examination
Week
13 3 CO 3 12. Sampling Theory Estimation of Parameters 1) Statistical Inference
Class discussion,
Problem Solving Problem Set
Examination All References
Course References:
A. Basic Readings
1. Walpole, R. (2014). Probability and statistics for engineers and scientists. Pearson Education: Singapore
2) Estimating the Mean
3) Estimating the Difference between Two Means 4) Estimating the Variance
and Lecture (Written) Assignment
Week
14 3
CO 3
13. Inferential Statistics 1) Test of Hypothesis
Statistical Hypothesis
Null and Alternative Hypotheses
One-Tailed and Two-Tailed Tests
Class discussion and
Lecture Examination
(Written) Recitation/Board
Work
All References
Week
15 3
CO 3
2) The Basic Steps in Hypothesis Testing
Tests Concerning Means, Variations and Proportions
Contingency Tables
Test for Independence
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written)
All References
Week 16
3
CO 3
14. Analysis of Variance Class discussion,
Problem Solving and Lecture
Problem Set Examination
(Written) Assignment
All References
Week
17-18 6
CO 3
15. Regression and Correlation
Class discussion, Problem Solving and Lecture
Problem Set Examination
(Written) Recitation/Board
Work
All References
Final Examination
2. Montgomery, D. (2011). Applied statistics and probability for engineers. John Wiley: Hoboken, NJ
B. Extended Readings
1. Hayter, A. (2013). Probability and statistics for engineers and scientists: with student solutions manual. Brooks/Cole Cengage Learning: Australia 2. Walpole, R. (2014). Essentials of probability and statistics for engineers and scientists. Pearson Education Limited: England
3. Johnson, R. (2011). Miller and Freund's probability and statistics for engineers. Pearson Education: Boston 4. Stark, H. (2012). Probability, statistics, and random processes for engineers. Pearson Education: Boston 5. Navidi, W. (2011). Statistics for engineers and scientists. McGraw-Hill: New York
C. Web References
Course Requirements
1) 3 Major Exams (Prelims, Midterms, and Finals) 2) Minimum of 6 Quizzes
3) Research Papers , Case Studies, Term Project
Grading System
Class Standing/Quizzes (60%) 3 Major Exams (40%)
TOTAL (100%) Passing Grade (50%)
CAMPUS++ COLLEGE ONLINE GRADING SYSTEM Legend: (All Items in Percent)
CSA Class Standing Average for All Performance Items (Cumulative) P Prelim Examination Score
M Midterm Examination Score F Final Examination Score MEA Major Exam Average PCA Prelim Computed Average MCA Midterm Computed Average FCA Final Computed Average
Computation of Prelim Computed Average (PCA)
CSA = (Sum of Raw Scores)/(Sum of Perfect Scores) x 100 MEA = P
PCA = (60%)(CSA) + (40%)(MEA)
Date Prepared: Date Effectivity: Prepared By: Checked By: Approved By:
Computation of Midterm Computed Average (MCA)
CSA = (Sum of Raw Scores)/(Sum of Perfect Scores) x 100 MEA = (P+ M)/2
MCA = (60%)(CSA) + (40%)(MEA)
Computation of Final Computed Average (FCA)
CSA = (Sum of Raw Scores)/(Sum of Perfect Scores) x 100 MEA = (P+ M+F)/3
FCA = (60%)(CSA) + (40%)(MEA)
Transmutation Table
Range of Computed Averages Range of Transmuted Values Grade General Classification
94.0000 – 100.0000 97 – 100 1.00 Outstanding
88.0000 – 93.9999 94 – 96 1.25 Excellent
82.0000 – 87.9999 91 – 93 1.50 Superior
76.0000 – 81.9999 88 – 90 1.75 Very Good
70.0000 – 75.9999 85 – 87 2.00 Good
64.0000 – 69.9999 82 – 84 2.25 Satisfactory
58.0000 – 63.9999 79 – 81 2.50 Fairly Satisfactory
52.0000 – 57.9999 76 – 78 2.75 Fair
50.0000 – 51.9999 75 3.00 Passed
Below Passing Average 5.00 Failed
6.00 Failure due to absences
8.00 Unauthorized or unreported withdrawal Note: A student's Computed Average is a consolidation of Class Standing Percent Average and Major Exam Percent Average.
Course Policies Maximum Allowable Absences: 10 (held 3 times a week); 7 (held 2 times a week)
June, 2016 June, 2016 Nikolai C. Cayanan
Faculty, General
Engineering Department
Engr. Filipina De Guzman
Chairperson, General Engineering Dept.
Melani B. Cabrera
Chair, IE Program
