SYLLABUS-MATH-212-DATA ANALYSIS-LEC-23-24.docx
- 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 1
- 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. 2
- 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. 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. 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 i. recognize the need for, and engage in life-long learning j. know contemporary issues. 3
- 4. k. use techniques, skills, and modern engineering tools necessary for engineering practice. l. knowledge and understand engineering and management principles as a member and leader of a team, and to manage projects in a multidisciplinary environment. 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: a b c d e f g h i j k l M CLO 1 Explain competently the concepts for probability theory and probability distribution. E E E E CLO 2 Apply accurately statistical knowledge in solving engineering problem situations and design experiments involving several factors. E E 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 4
- 5. V. Course Coverage: WEEK CLO Code Link TOPIC TEACHING & LEARNING ACTIVITIES (TLA) ASSESSMENT TARGET 1 2 3 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/Quiz 60% 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/Quiz 60% of the students shall have a rating of at least 70% WEEK CLO Code Link TOPIC TEACHING & LEARNING ASSESSMENT TARGET 5
- 6. ACTIVITIES (TLA) 1 2 3 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/Quiz 60% 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/Quiz 60% of the students shall have a rating of at least 70% 6
- 7. WEEK CLO Code Link TOPIC TEACHING & LEARNING ACTIVITIES (TLA) ASSESSMENT TARGET 1 2 3 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/Quiz 60% 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/Quiz 60% 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/Quiz 60% 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) 1 2 3 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/Quiz 60% 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/Quiz 60% of the students shall have a rating of at least 70% WEEK CLO Code TOPIC TEACHING & ASSESSMENT TARGET 8
- 9. Link LEARNING ACTIVITIES (TLA) 1 2 3 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/Quiz 60% 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/Quiz 60% 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) 1 2 3 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/Quiz 60% 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/Quiz 60% of the students shall have a rating of at least 70% FINAL EXAM Written Exam VI. Course Requirements: 10
- 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 11 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 12
- 13. 2. Walpole, Ronald E. , Probability & Statistics for Engineer 9th Edition 3. Montgomery, Douglas C., Applied Statistics and Probability for Engineers 6th 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 Council President / Dean RUBRICS : 1. Rubrics for recitation POINTS INDICATORS 5 Gives and state answer clearly 13
- 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 calculation Checking back the result 0 Misinterpretation or incorrect at all Not performing calculation Not 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) = __________ 14