Teaching Guide 2022 Summer - 11381 Regression Analysis Online Mode - Universidad Católica San Antonio de Murcia - Tlf: (+34) 968 278 160 ...
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Regression Analysis
Teaching Guide 2022 Summer
11381 Regression Analysis
Online Mode
Universidad Católica San Antonio de Murcia – Tlf: (+34) 968 278 160 info@ucam.edu– www.ucam.edu
Statistics- Tlf: (+34) 902 102 101Regression Analysis
11381 Regression Analysis
Course Information
Module: Regression Analysis
Field: Statistics
Character: Introductory Training
Credits: 4
Course Instructor Information
Teacher: TBA
Office hours: 55 Hours
Student’s attention timetable: Monday to Friday, 10h - 12h
Module coordinator teacher: To Be Assigned
Brief Description
The course gives an detailed introduction to various areas of
concepts, theories and methods of linear and nonlinear regression
analysis. Topics of the course include simple linear regression,
multiple linear regression, polynomial regression, model adequacy
checking, residuals analysis, least squares estimators, variable
selection and model building, regression models, nonlinear least
squares and regression analysis of time series data.
Previous Requisite(s)
11807 Introduction to Probability and Statistics
Statistics- Tlf: (+34) 902 102 101Regression Analysis
Competences and Learning Results
1. Cross Curricular Competences
(1) Analysis and synthesis skills;
(2) Planning and organizational skills;
(3) Problem solving skills;
(4) Decision making skills;
(5) Information management skills;
(6) Computer science knowledge related to the field of study;
(7) Capacity for critical thinking;
(8) Autonomous learning;
(9) Motivation for quality;
(10) Reflection ability.
2. Learning Results
Having successfully completed this course, students will be able to:
1. Understand the basic concepts in probability and statistics,
linear and nonlinear regression analysis;
2. Acquire skills in scientific methods, critical reasoning and
problem solving;
3. Use matrix operations to analyze variance of linear combination
of random variables;
4. Have the ability to apply the knowledge to the practice in real
life.
Statistics- Tlf: (+34) 902 102 101Regression Analysis
3. Specific Competences
1. Know and apply the basic concepts of Statistics;
2. Develop logic thinking and critical analysis;
3. Communicate fluently within the work environment and work
in team;
4. Improve skills in critical reasoning and argumentative writing.
Methodology
Hours of work Hours of work
Methodology Hours
Face-to-face Non Face-to-face
Lectures 50
88 hours (60%)
Practice teaching 8
Assessment 30
Personal study 30
68 hours (40%)
Tasks 22
Practical teaching 10
preparation
Bibliographic search 6
TOTAL 156 88 68
Required Textbook(s)
Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining,
Introduction to Linear Regression Analysis, 5th Edition, Wiley, 2012.
Statistics- Tlf: (+34) 902 102 101Regression Analysis
Class Schedule
Week Lesson Content
1 Course Introduction
Simple Linear Regression
2
Properties of the Least-Squares Estimators
Multiple Linear Regression
3
1 Properties of the Least-Squares Estimators
4 Model Adequacy Checking
Transformations and Weighting to Correct Model
5 Inadequacies
Quiz 1
Transformations to Linearize the Model
6 Generalized and Weighted Least Squares
Analytical Methods for Selecting a Transformation
7 Diagnostics for Leverage and Influence
2 8 Polynomial Regression Models
Indicator Variables
9
Regression Approach to Analysis ofVariance
Multicollinearity
10
Assignment 1 due
11 Variable Selection and Model Building
12 Midterm Test Reviews
3 13 Midterm Test
14 Validation ofRegression Models
15 Introduction to Nonlinear Regression
16 Nonlinear Least Squares
17 Generalized Linear Models
18 Logistic Regression Models
4
19 Poisson Regression
The Generalized Linear Model
20
Quiz 2
Statistics- Tlf: (+34) 902 102 101Regression Analysis
21 Regression Analysis of Time Series Data
22 Other Topics in the Use ofRegression Analysis
Effect ofMeasurement Errors in the Regressors
5 23 Designed Experiments for Regression
Assignment 2 due
24 Final Exam Reviews
25 Final Exam
Rating System
1. Assessment
ASSESSMENT ITEM PERCENT OF FINAL GRADE
2 Quizzes 20% (10% for each)
2 Assignments 20% (10% for each)
Midterm Test 25%
Final Exam 35%
2. Grading Scale
A+ 96-100 A 90-95 A- 85-89
B+ 82-84 B 78-81 B- 75-77
C+ 71-74 C 66-70 C- 62-65
D 60-61 F < 60
Statistics- Tlf: (+34) 902 102 101Regression Analysis
General Expectations
Students are expected to:
Attend all classes and be responsible for all materials covered in class and
otherwise assigned;
Complete the daily required reading and assignments before class;
Review the previous class notes before class and make notes about questions you
have about the previous class or the course reading;
Participate in class discussions and complete required written work on time;
Refrain from texting, phoning or engaging in computer activities unrelated to class
during the class period;
While class participation is welcome, even required, you are expected to refrain
from private conversations during the class period.
Attending Policy
Regular and prompt attendance is required. Attendance will be taken at the start of the
course. Those that miss their name, during roll call, will be counted as absent.
Students can miss up to three classes (including labs) and earn 7% (out of 10%). After
the third absence, students will earn a grade of 0% (out of 10%). Arriving late and/or
leaving before the end of the class are equivalent to absences.
Policy on “Late Withdrawals”
In accordance with the policy ofUCAM, appeals for late withdrawal will be approved
ONLY in case of medical emergency and similar crises.
Statistics- Tlf: (+34) 902 102 101Regression Analysis
Academic Honesty
All students are expected to respect academic honesty policy. Instructors will fail
assignments that show any evidence of plagiarism or other forms of cheating and will
also report the student's information to the University Administration Office. A
student reported to the University for cheating will be placed on the list of disciplinary
probation; a student reported twice will be suspended or expelled.
Special Needs or Assistance
Please contact the University Administrative Office immediately if you have a
learning disability, a medical issue, or any other type of problem that prevents
professors from seeing you have learned the course material.
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