Bivariate Regression
Linear Regression Models
1
SPSS for Windows® Intermediate & Advanced Applied Statistics Zayed University Office of Research SPSS for Windows® Workshop Series Presented by Dr. Maher Khelifa Associate Professor Department of Humanities and Social Sciences College of Arts and Sciences
© Dr. Maher Khelifa
2
Bi-variate Linear Regression
(Simple Linear Regression)
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
3
Many statistical indices summarize information about particular
phenomena under study.
For example, the Pearson (r) summarizes the magnitude of a linear
relationship between pairs of variables.
However, one major scientific research objective is to “explain”,
“predict”, or “control” phenomena.
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
4
To explain, predict, and control phenomena, we must not view
variables in isolation.
How variables do or do not relate to other variables provide us with
valuable clues which allow us to:
Explain Predict, and Control
The examination of these relationships leads to the formation of
networks of variables that provide the basis for the development of theories about a phenomenon.
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
5
Linear regression analyses are statistical procedures which allow us to
move from description to explanation, prediction, and possibly control.
Bivariate linear regression analysis is the simplest linear regression
procedure.
The procedure is called simple linear regression because the
model:
explores the predictive or explanatory relationship for only 2 variables, and Examines only linear relationships.
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
6
Simple linear regression focuses on explaining/ predicting one of the
variables on the basis of information on the other variable.
The regression model
1
SPSS for Windows® Intermediate & Advanced Applied Statistics Zayed University Office of Research SPSS for Windows® Workshop Series Presented by Dr. Maher Khelifa Associate Professor Department of Humanities and Social Sciences College of Arts and Sciences
© Dr. Maher Khelifa
2
Bi-variate Linear Regression
(Simple Linear Regression)
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
3
Many statistical indices summarize information about particular
phenomena under study.
For example, the Pearson (r) summarizes the magnitude of a linear
relationship between pairs of variables.
However, one major scientific research objective is to “explain”,
“predict”, or “control” phenomena.
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
4
To explain, predict, and control phenomena, we must not view
variables in isolation.
How variables do or do not relate to other variables provide us with
valuable clues which allow us to:
Explain Predict, and Control
The examination of these relationships leads to the formation of
networks of variables that provide the basis for the development of theories about a phenomenon.
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
5
Linear regression analyses are statistical procedures which allow us to
move from description to explanation, prediction, and possibly control.
Bivariate linear regression analysis is the simplest linear regression
procedure.
The procedure is called simple linear regression because the
model:
explores the predictive or explanatory relationship for only 2 variables, and Examines only linear relationships.
© Dr. Maher Khelifa
Understanding Bivariate Linear Regression
6
Simple linear regression focuses on explaining/ predicting one of the
variables on the basis of information on the other variable.
The regression model
References: 58 SPSS Base 7.0 Application Guide (1996). Chicago, IL: SPSS Inc. SPSS Base 7.5 For Windows User’s Guide (1996). Chicago, IL: SPSS Inc. SPSS Base 8.0 Application Guide (1998). Chicago, IL: SPSS Inc. SPSS Base 8.0 Syntax Reference Guide (1998). Chicago, IL: SPSS Inc. SPSS Base 9.0 User’s Guide (1999). Chicago, IL: SPSS Inc. SPSS Base 10.0 Application Guide (1999). Chicago, IL: SPSS Inc. SPSS Base 10.0 Application Guide (1999). Chicago, IL: SPSS Inc. SPSS Interactive graphics (1999). Chicago, IL: SPSS Inc. SPSS Regression Models 11.0 (2001). Chicago, IL: SPSS Inc. SPSS Advanced Models 11.5 (2002) Chicago, IL: SPSS Inc. SPSS Base 11.5 User’s Guide (2002). Chicago, IL: SPSS Inc. SPSS Base 12.0 User’s Guide (2003). Chicago, IL: SPSS Inc. SPSS 13.0 Base User’s Guide (2004). Chicago, IL: SPSS Inc. SPSS Base 14.0 User’s Guide (2005). Chicago, IL: SPSS Inc.. SPSS Base 15.0 User’s Guide (2007). Chicago, IL: SPSS Inc. SPSS Base 16.0 User’s Guide (2007). Chicago, IL: SPSS Inc. SPSS Statistics Base 17.0 User’s Guide (2007). Chicago, IL: SPSS Inc. © Dr. Maher Khelifa