Fndstat course syllabus v 1 PDF

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DE LA SALLE UNIVERSITYGOKONGWEI COLLEGE OF ENGINEERINGDEPARTMENT of INDUSTRIAL ENGINEERINGFNDSTATCourse SyllabusCourse Title: Foundation Course in Statistics Credit Units: Three (3) Prerequisite: K-12 Statistics Description: The course covers the fundamental concepts of data collection and statistic...

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DE LA SALLE UNIVERSITY GOKONGWEI COLLEGE OF ENGINEERING DEPARTMENT of INDUSTRIAL ENGINEERING FNDSTAT Course Syllabus Course Title: Foundation Course in Statistics Credit Units: Three (3) Prerequisite: K-12 Statistics Description: The course covers the fundamental concepts of data collection and statistical analysis. It covers probability, random variables, special discrete and continuous probability distributions, statistical hypothesis testing, with linear regression and correlation analysis. It also uses the results of computation technology and their interpretation. Learning Outcomes and Graduate Attributes: Upon the completion of the course, the student is expected to be able to do the following: EXPECTED LASALLIAN STUDENT OUTCOME (SO) LEARNING OUTCOME GRADUATE (LO) ATTRIBUTES (ELGA) As a critical and A - An ability to apply 1. Explain the process of data collection and creative thinker knowledge of mathematics, statistical thinking on the types of data physical and information variables and the qualitative and sciences, and engineering quantitative measures of said variables. sciences to the practice of 2. Determine statistical measures of central industrial engineering tendency (mean, median, mode) and dispersion (standard deviation, range, interquartile range) through hand computation and through technology. 3. Compute and interpret probabilities using empirical and classical method. 4. Distinguish between various probability distributions and determine their expected values for a random variable. 5. Distinguish the different statistical hypothesis testing situations, and determine the correct test statistic under each. 6. Recognize and interpret subjective probability statements as distinguished from empirical and classical probabilities. 7. Use probability concepts in daily decisionmaking.

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FNDSTAT Course Syllabus

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Course Assessment Matrix:

Learning Outcomes

Student Outcomes

A B LO1 1 LO2 1 LO3 1 LO4 1 LO5 1 LO6 1 LO7 1 Legend: 1 = Introductory

C

D

E

F

2=Reinforcing

G

H

I

J

K

3=Emphasizing

Performance Indicators:

Performance Indicator A1 A2 A1: Identify appropriate mathematical tools to use. A2: Execute algorithmic procedure correctly

Assessment Task Quiz 1, Quiz 2, Quiz 3 Quiz 1, Quiz 2, Quiz 3

Requirements and Assessments: The student will be assessed at other times during the term by the following: ● Quizzes and Final Exam ● Assignments Grading System: Average of three (3) quizzes Final Exam Assigned Homework TOTAL Passing is 60%

60 % 30 % 10 % 100%

Learning Plan:

LERNING OUTCOME (LO) LO1, LO2

LO3, LO7

TOPIC 1.0 Statistical Measures of Central Tendency and Dispersion 1.1 Population Parameters vs. Sample Statistics 1.2 Arithmetic Mean, Median and Mode 1.3 Standard Deviation, Variance, Quartiles, Skewness, Kurtosis 2.0 Classical Probability and Counting Techniques 2.1 Review of Counting Principles and Techniques 2.1.1 Multiplication Rule

WEEK NO. 1

2-3

LEARNING ACTIVITIES Lectures Computational Sessions Demonstration using Excel Assignments

Lectures Computational Sessions Assignments

L

FNDSTAT Course Syllabus

LERNING OUTCOME (LO)

LO4, LO7

LO3, LO4

LO3, LO4

LO3, LO5, LO6

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TOPIC 2.1.2 Addition Rule and Mutually exclusive Cases 2.1.3 Permutations 2.1.4 Combinations 2.1.5 Special Permutation Cases 2.2 Contingency Tables 2.3 Classical Probability Formula 2.4 Probability Laws 3.0 Random Variables and Expectation 3.1 Concepts and Types of Random Variables: Categorical, Discrete and Continuous 3.2 Mean and Variance 3.3 Cumulative Distribution Function - F(X) Quiz 1 4.0 Discrete Probability Distributions: Function, Expected value and variance 4.1 Discrete Uniform Distribution 4.2 Binomial Distribution 4.3 Hypergeometric Distribution 4.4 Poisson Distribution: occurrence of events 5.0 Continuous Probability Distributions: Function, Expected value and variance 5.1 Continuous Uniform Distribution and computing probabilities using F(X) 5.2 Normal Distribution: the template of nature 5.3 Central Limit Theorem 5.4 Exponential Distribution and computing probabilities using F(X) 6.0 Sampling and Census 6.1 Sampling procedures and Randomization against bias 6.2 Probabilistic Sampling: Simple Random Sampling, Stratified sampling, Cluster sampling

WEEK NO.

LEARNING ACTIVITIES

4

Lectures Computational Sessions Assignments

5-6a

Lectures Computational Sessions Assignments

6b-7

Lectures Computational Sessions Assignments

8

Lectures Computational Sessions Assignments

FNDSTAT Course Syllabus

LERNING OUTCOME (LO)

LO5, LO6

LO5, LO6

LO7

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TOPIC

WEEK NO.

6.3 Non-probabilistic sampling: Systematic sampling, convenience sampling Quiz 2 7.0 Hypothesis Testing: Single Population 7.1 Review of Steps in Making a Hypothesis Test 7.1.1 Concept of Significance Level  7.1.2 Concept of type I and Type II Error 7.2 Single Sample test of hypothesis for mean : Large Sample (n≥30) 7.3 Single Sample test of hypothesis for mean : Small Sample (n...