REGRESSION ANALYSIS

BTS584

Course Code: BTS584
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree Without Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Should do the basic statistical analyses and interpret the results

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

There is no recommended optional programme component for this course.

Course Description

Simple linear regression, coefficients of correlation and determination, multiple regression, multiple coefficients of correlation and determination, adjusted coefficient of determination, partial correlation. Nonlinear regression models and linearization transformations, hypothesis tests and confidence intervals. Residual analysis; plotting the residuals, multicollinearity, detecting the unequal variances, checking normality assumption, dummy variables, autocorrelation, variable selection methods in multiple regression models and model building.

Course Contents

WEEK	TOPICS
1^st Week	Introduction to regression analysis; aims of regression analysis; types of data in regression analysis
2^nd Week	Simple linear regression; MSE method; model assumptions; estimation of ; estimation, confidence intervals and testing of regression parameters
3^rd Week	Simple linear regression; MSE method; model assumptions; estimation of ; estimation, confidence intervals and testing of regression parameters
4^th Week	Multiple regression; MSE method; model assumptions; estimation of ; estimation, confidence intervals and testing of regression parameters
5^th Week	Multiple coefficient of determination; using multiple regression models for estimation and prediction
6^th Week	Multiple coefficient of determination
7^th Week	Multiple coefficient of determination
8^th Week	MIDTERM
9^th Week	Model models with quantitative and qualitative variables building;
10^th Week	Model models with quantitative and qualitative variables building;
11^th Week	Model models with quantitative and qualitative variables building;
12^th Week	Residual analysis; plotting the residuals; detecting the unequal variances;
13^th Week	Multicollinearity; causes of multicollinearity; effects of multicollinearity
14^th Week	Variable selection methods

Programme Qualifications / Learning Outcomes

	LO1
K1
K2
K3
K4
K5
K6	X
K7	X
K8	X
K9	X
K10
K11