SYLLABUS-MATH-212-DATA ANALYSIS-LEC-23-24.docx

Syllabus

1. GARCIA COLLEGE OF TECHNOLOGY
Kalibo, Aklan
CIVIL ENGINEERING DEPARTMENT
INSTITUTIONAL VISION
Garcia College of Technology envisions to help men and women achieve their dreams so that they can contribute to the development of our society.
INSTITUTIONAL MISSION
Garcia College of Technology is committed to:
a. provide quality education;
b.develop the full potentialities and capabilities of the individual.
PROGRAM EDUCATIONAL OBJECTIVES (PEO) MISSION
Within 3 to 5 years after graduation, the program expects that the Civil Engineering graduates will: a b
1) be employed in civil engineering careers with companies and organizations in industry, government, non-governmental organizations,
and entrepreneurial ventures; applying their ability to plan, design, and manage the construction of civil engineering works;
 
2) engage in the practice of civil engineering profession with awareness and commitment to economical , environmental, ethical, and
societal considerations as well as professional standards, by applying acquired civil engineering knowledge.
 
3) advance their skills through professional growth and development activities such graduate study in engineering and continuing education 
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2. COURSE SYLLABUS IN ENGINEERING DATA ANALYSIS
First Semester, A.Y. 2023 – 2024
I. Course Title: Engineering Data Analysis
Course Code: Math 212
Credit Unit: 3 units (lecture)
No. of Contact Hours per week: 3 hours lecture per week
Prerequisite: Math 111
Co-requisite: None
II.Course Description:
This course is designed for undergraduate engineering students with emphasis on problem solving related to societal issues that engineers and scientists are called
upon to solve. It introduces different methods of data collection and the suitability of using a particular method for a given situation.
The relationship of probability to statistics is also discussed, providing students with the tools they need to understand how "chance" plays a role in statistical
analysis. Probability distributions of random variables and their uses are also considered, along with a discussion of linear functions of random variables within
the context of their application to data analysis and inference. The course also includes estimation techniques for unknown parameters; and hypothesis testing used
in making inferences from sample to population; inference for regression parameters and build models for estimating means and predicting future values of key
variables under study. Finally, statistically based experimental design techniques and analysis of outcomes of experiments are discussed with the aid of statistical
software.
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3. III. Students/Program Outcome (PO) and Relationship to Program Educational Objectives (PEO):
Program Educational
Objective
By the time of graduation, the students of the program shall have the ability to: 1 2 3
a.apply knowledge of mathematics, physical, life and information sciences; and engineering sciences appropriate to the field
of practice.
 
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b.design and conduct experiments as well as to analyze and interpret data.
 

c.design a system, component, or process to meet desired needs within identified constraints.
 

d.function in multi-disciplinary and multi- cultural teams
 

e.identify, formulate, and solve complex civil engineering problems.
 

f.understand professional and ethical responsibility.
 
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g.communicate effectively civil engineering activities with the engineering community and with society at large;
 

h.understand the impact of civil engineering solutions in a global, economic, environmental, and societal context
 
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i.recognize the need for, and engage in life-long learning
 

j.know contemporary issues.  
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4. 
k. use techniques, skills, and modern engineering tools necessary for engineering practice.
 
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l.knowledge and understand engineering and management principles as a member and leader of a team, and to manage
projects in a multidisciplinary environment.
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m.Understand at least one specialized field of civil engineering practice
 

IV. Course Learning Outcomes (CLO):
Course Learning Outcomes (CLO) Program Outcome (PO)
At the end of the course, the student should be able to: abcdefghijklM
CLO 1 Explain competently the concepts for probability theory and probability distribution.EE E E
CLO 2 Apply accurately statistical knowledge in solving engineering problem situations and design
experiments involving several factors.
EE E E
CLO 3 Describe knowledgeably the methods associated with regression, correlation, construction,
simulation, and analysis of probability models.
D D D D
I = Introductory E = Enable D = Demonstrate
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5. V. Course Coverage:
WEEK
CLO Code
Link
TOPIC
TEACHING &
LEARNING
ACTIVITIES
(TLA)
ASSESSMENT TARGET
123
1
Orientation Discussion/
Powerpoint
presentation
1 to 2

1. Obtaining Data
1.1 Methods of Data Collection
1.2 Planning and Conducting Surveys
1.3 Planning and Conducting Experiments: Introduction to Design of
Experiments
Lecture/Discussion/
Powerpoint
presentation/
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
3

2. Probability
2.1 Sample Space and Relationships among Events
2.2 Counting Rules Useful in Probability
2.3 Rules of Probability
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
WEEK CLO Code
Link
TOPIC TEACHING &
LEARNING
ASSESSMENT TARGET
5

6. ACTIVITIES
(TLA)
123
4 
3. Discrete Probability Distributions
3.1 Random Variables and their Probability Distributions
3.2 Cumulative Distribution Functions
3.3 Expected Values of Random Variables
3.4 The Binomial Distribution
3.5 The Poisson Distribution
Recitation/Quiz60% of the students
shall have a rating of at
least 70%
PRELIM EXAMINATION Written
Examination
60% of the students
shall have a rating of at
least 70%
5 
4. Continuous Probability Distribution
4.1. Continuous Random Variables and their Probability
Distribution
4.2. Expected Values of Continuous Random Variables
4.3. Normal Distribution
4.4. Normal Approximation to the Binomial and Poisson
Distribution
4.5. Exponential Distribution
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the students
shall have a rating of at
least 70%
6

7. WEEK
CLO Code
Link
TOPIC
TEACHING &
LEARNING
ACTIVITIES
(TLA)
ASSESSMENT TARGET
123
6 
5. Joint Probability Distribution
5.1. Two or Random Variables
5.1.1. Joint Probability Distributions
5.1.2. Marginal Probability Distribution
5.1.3. Conditional Probability Distribution
5.1.4. More than Two Random Variables
5.2. Linear Functions of Random Variables
5.3. General Functions of Random Variables
Lecture/Discussion/
Powerpoint
presentation
Boardwork
Recitation/Quiz60% of the students
shall have a rating of at
least 70%
7

6. Sampling Distributions and Point Estimation of Parameters
6.1. Point Estimation
6.2. Sampling Distribution and the Central Limit Theorem
6.3. General Concept of Point Estimation
6.3.1. Unbiased Estimator
6.3.2. Variance of a Point Estimator
6.3.3. Standard Error
6.3.4. Mean Squared Error of an Estimator
Lecture/Discussion/
Powerpoint
presentation
Boardwork
Recitation/Quiz60% of the students
shall have a rating of at
least 70%
8

Statistical Intervals
7.1. Confidence Intervals: Single Sample
7.2. Confidence Intervals: Multiple Samples
7.3. Prediction Intervals
7.4. Tolerance Intervals
Lecture/Discussion/
Powerpoint
presentation
Boardwork
Recitation/Quiz60% of the students
shall have a rating of at
least 70%
MIDTERM EXAM
WEEK CLO Code
Link
TOPIC TEACHING &
LEARNING
ASSESSMENT TARGET
7

8. ACTIVITIES
(TLA)
123
9

8. Test of Hypothesis for a Single Sample
8.1. Hypothesis Testing
8.1.1. One-sided and Two-sided Hypothesis
8.1.2. P-value in Hypothesis Tests
8.1.3. General Procedure for Test of Hypothesis
8.2. Test on the Mean of a Normal Distribution, Variance Known
8.3. Test on the Mean of a Normal Distribution, Variance Unknown
8.4 Test on the Variance and Statistical Deviation of a Normal
Distribution
8.5. Test on a Population Proportion
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
10

9. Statistical Inference of Two Samples
9.1. Inference on the Difference in Means of Two Normal
Distributions, Variances Known
9.2. Inference on the Difference in Means of Two Normal
Distributions, Variances Unknown
9.3. Inference on the Variance of Two Normal Distributions
9.4. Inference on Two Population Proportions
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
WEEK CLO Code TOPIC TEACHING & ASSESSMENT TARGET
8

9. Link
LEARNING
ACTIVITIES
(TLA)
123
11 to 12

10. Simple Linear Regression and Correlation
10.1. Empirical Models
10.2. Regression: Modelling Linear Relationships – The Least
Squares Approach
10.3. Correlation: Estimating the Strength of Linear Relation
10.4. Hypothesis Tests in Simple Linear Regression
10.4.1. Use of t-tests
10.4.2. Analysis of Variance Approach to Test Significance of
Regression
10.5. Prediction of New Observations
10.6. Adequacy of the Regression Model
10.6.1. Residual Analysis
10.6.2. Coefficient of Determination
10.7. Correlation
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
13

11. Multiple Linear Regression
11.1. Multiple Linear Regression Model
11.2. Hypothesis Test in Multiple Linear Regression
11.3. Prediction of New Observations
11.4. Model Adequacy Checking
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
PRE-FINAL EXAM Written Exam
WEEK CLO Code
Link
TOPIC TEACHING &
LEARNING
ASSESSMENT TARGET
9

10. ACTIVITIES (TLA)
123
14 to 15

12.Design and Analysis of Single Factor Experiments
12.1. Completely Randomized Single Factor Experiments
12.1.1. Analysis of Variance (ANOVA)
12.1.2. Multiple Comparisons following the ANOVA
12.1.3. Residual Analysis and Model Checking
12.1.4. Determining Sample Size
12.2. The Random-Effects Model
12.2.1. Fixed versus Random Factors
12.2.2. ANOVA and Variance Components
12.3. Randomized Complete Block Design
12.3.1. Design and Statistical Analysis
12.3.2. Multiple Comparisons
12.3.3. Residual Analysis and Model Checking
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
16 to 17

13. Design of Experiments with Several Factors
13.1. Factorial Experiments
13.2. Two-Factor Factorial Experiments
13.2.1. Statistical Analysis of the Fixed-Effects Model
13.2.2. Model Adequacy Checking
13.3. 2 k Factorial Design
13.3.1. Single Replicate
13.3.2. Addition of Center Points
13.4. Blocking and Confounding in the 2k Design
13.5. Fractional Replication of the 2k Design
13.6. Response Surface Method
Lecture/Discussion/
Powerpoint
presentation
Board work
Recitation/Quiz60% of the
students shall
have a rating of at
least 70%
FINAL EXAM Written Exam
VI. Course Requirements:
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11. Class standing requirements include quizzes, major examinations, laboratory/fieldwork activities, oral recitation, and assignments. Quizzes are
announced and major examinations are scheduled.
Grading System:(Zero-based grading system)
MES = 0.50 QAve + 0.5 (
PE+ME
2
)
FES = 0.4 QAve + 0.45 (
PE+ME+PFE+FE
4
) +0.10 OR &/or WR + 0.05 ASS
Where:
QAVE = quiz average during the period
PE = prelim exam ASS = assignment
MES = midterm equivalent score
PFE = pre-final equivalent score
FE = final exam
FES = final equivalent score
OR = oral recitation
WR = written report
CUT-OFF SCORE: 50
Cut-off Score: 50
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TABLE OF EQUIVALENT GRADE:
Final Equivalent
Score(FES)
Grade Description
99-100 1.00 Exceptional
94-98 1.25 Excellent
87-93 1.50 Superior
81-86 1.75 Very Good
75-80 2.00 Good
69-74 2.25 Satisfactory
62-68 2.50 Average
56-61 2.75 Fair
50-55 3.00 Passing
Below 50 5.00 Failure
7.00 Incomplete
9.00 Dropped

12. VII. Learning Resources:
Book/References:
Author, Title, Publisher, Place of Publication, Date of Publication
1. Robinson,Edward L., Data Analysis for Scientists and Engineers
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13. 2. Walpole, Ronald E. , Probability & Statistics for Engineer 9
th
Edition
3. Montgomery, Douglas C., Applied Statistics and Probability for Engineers 6
th
Edition
Prepared by: Evaluated by: Noted by: Approved by:
Engr. JOSEFINA R. DAGOHOY Engr. JOSEFINA R. DAGOHOY Engr. JOLENIE I. HING Dr. ARNULFO Q. DISTOR, JR.
Instructor Chair, CE Department Head of Academic CouncilPresident / Dean
RUBRICS :
1.Rubrics for recitation
POINTSINDICATORS
5 Gives and state answer clearly
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14. 4 State answer only
3 Answers abruptly
2 Try to give the answer even if it is not correct
1 Refuse to participate/answer
2.Rubrics for problem solving
Score Understanding problem Performing calculationChecking back the result
0 Misinterpretation or incorrect at allNot performing calculationNot checking back
1 Misinterpretation partially, disregard of
problem condition
Performing the right
procedure and probably
produce a correct answer but
miscalculate
Checking back but
incomplete
2 Constructing the right plan but incorrect
in the result or no result
Performing the right
procedure and getting a
correct answer
Checking back to see the
validity of process
TOTAL SCORE = __________
PERCENTAGE RATING = (
TOTALSCORE
6 )
(100) = __________
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