Stat 332

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

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
Course Calendar

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

 
 
 
Assignments, project and Exams:

Assignments and projects Will be given during the  classes 10 marks
Midterm Exam I   25 marks
Midterm Exam II   25 marks
Final Exam   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