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

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2. Shown a commitment to life-long learning

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3. Manifested faithful stewardship

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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.

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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

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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.

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h) Broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context

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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

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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

Dr. Maria Doris C. Bacamante Dean, CEA