Stat 332

 

Outline of Stat 332
 

Regression Analysis

                                                                    

Instructor:  Prof. Khalaf S. Sultan

Office:  2B20 Building #4,  Phone (office): 4676263

E-mail:  [e-mail]       

Recommended Books: 

Applied Linear Statistical Models, John Neter, William Wasserman, Michael Kutner and Willism Li, 5th edition

Download the book data sets
 

  نماذج إحصائية خطية تطبيقية -  الجزء الأول
المؤلف:  نيتر واخرون.ترجمة: د. انيس كنجو – د. عبد الحميد الزيد – د. الحسيني عبد البر

Course  Contents: 

This course is an introduction to applied data analysis. We will explore data sets, examine various models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. We then consider simple linear regression, a model that uses only one predictor. After briefly reviewing some linear algebra, we turn to multiple linear regression, a model that uses multiple variables to predict the response of interest. For all models, we will examine the underlying assumptions. More specifically, do the data support the assumptions? Do they contradict them? What are the consequences for inference? Also, we will explore some nonlinear models and data transformations. Finally, we discuss Linear regression based on the categorical with some applications

Course Calendar

Week	Date	Topics Covered
1	26/12/1438	Introduction and some basic concepts of probability and statistics
2	4/1/1439	Definition of the Simple linear regression model with some applications
3	11/1/1439	Estimation of the unknown parameters of the simple linear regression model
4	18/1/1439	Properties of the least square method
5	25/1/1439	Confidence estimation of the least square estimated of the coefficient of simple linear regression model.
6	2/2/1439	Hypotheses Testing of the simple linear regression model
7	9/2/1439	The efficiency of the simple linear regression model by using ANOVA
8	16/2/1439	Predication and residual analysis of the simple linear regression model
Mid-1  Sunday 16/2/1439,  7:00 -8:30 pm
9	23/2/1439	Multiple linear regression model
10	1/3/1439	Estimation of the unknown parameters of the multiple linear regression model.
11	8/3/1439	Hypothesis testing of the multiple linear regression model
12	15/3/1439	Prediction and residual analysis of the multiple linear regression model
Mid-2  Sunday 22/3/1439,  7:00 -8:30 pm
13	22/3/1439	Linear regression based on the categorical with some application
14	29/3/1439	Applications
15	6/4/1439	Revision
Final Exam Monday 21/4/1439,  1:00 -4:00pm

 Assignments, project and Exams:

Assignments and projects	Will be given during the  classes	12marks
Midterm Exam I	Mid-1  Sunday 16/2/1439,  7:00 -8:30 pm	24 marks
Midterm Exam II	Mid-2  Sunday 22/3/1439,  7:00 -8:30 pm	24 marks
Final Exam	Final Exam Monday 21/4/1439,  1:00 -4:00pm	40 marks

 

Computing:

In this course, we will use R language.

Attendance:
Students missing more than 25% of the total class hours won't be allowed to write the final exam.