Stat 332

 

Outline of Stat 332
 

Regression Analysis

Instructor:  Prof. Khalaf S. Sultan
Office:  2B20 Building #4,  Phone (office): 4676263
 E-mail:  ksultan@ksu.edu.sa       
Textbook: 
Applied Linear Regression Models, Fifth Edition by Kutner, Nachtsheim and Neter
  كتاب مترجم للطبعة الرابعة
نماذج إحصائية خطية تطبيقية ( الجزء الأول)  
المؤلف:  نيتر واخرون .ترجمة: د. انيس كنجو – د. عبد الحميد الزيد – د. الحسيني عبد البر

Book DATA

 
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 2/9/2018 Introduction and some basic concepts of probability and statistics
2 16/9/2018 Definition of the Simple linear regression model with some applications
3 23/9/2018 Estimation of the unknown parameters of the simple linear regression model
4 30/9/2018 Properties of the least square method
5 7/10/2018 Confidence estimation of the least square estimated of the coefficient of simple linear regression model.
6 14/10/2018 Hypotheses Testing of the simple linear regression model
7 21/10/2018 The efficiency of the simple linear regression model by using ANOVA
8 28/10/2018 Predication and residual analysis of the simple linear regression model
9 4/11/2018 Multiple linear regression model
10 11/11/2018 Estimation of the unknown parameters of the multiple linear regression model.
11 18/11/2018 Hypothesis testing of the multiple linear regression model
12 25/11/2018 Prediction and residual analysis of the multiple linear regression model
13 2/12/2018 Linear regression based on the categorical with some application
14 9/12/2018 Applications
15 16/12/2018 Revision

 
 
 
Assignments, project and Exams:

Assignments and projects Will be given during the  classes 10 marks
Midterm Exam I Sunday 12/2/1440 (5:00-6:30pm) 
 
25 marks
Midterm Exam II Sunday 10/3/1440 (5:00-6:30pm) 
 
25 marks
Final Exam Monday 14/4/1440 (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. 
 
 

 
 

Course Materials