The aim with regression analysis is to summarize the observed data and study how the response of a dependent variable varies as the values of the independent variable(s) change. There are many models that examine this relationship by obtaining the estimates of parameters in a regression model. The classical linear regression model (CLRM) is the basis of all the other models discussed in this book. This chapter discusses the CLRM in detail using the ordinary least squares (OLS) estimation method. The outcome of OLS can also be used as a benchmark in more advanced analysis. The focus is on the assumptions and applications of this technique, starting from a single-regression model with one independent variable and then covering multiple linear regression models with many independent variables. The chapter provides an application to the capital asset pricing model, lab work on the CLRM, and a mini case study.
Review the options below to login to check your access.
Log in with your Cambridge Higher Education account to check access.
If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.