Simple Linear Regression
Refresher reading access
Introduction
Financial analysts often need to examine whether a variable is useful for explaining another variable. For example, the analyst may want to know whether earnings or cash flow growth help explain a company’s market value. Regression analysis is a tool for examining this type of issue.
Linear regression allows us to test hypotheses about the relationship between two variables by quantifying the strength of the relationship between the two variables, and to use one variable to make predictions about the other variable. Our focus is on linear regression with a single independent variable—that is, simple linear regression.
Learning Outcomes
The candidate should be able to:
- describe a simple linear regression model, how the least squares criterion is used to estimate regression coefficients, and the interpretation of these coefficients
- explain the assumptions underlying the simple linear regression model, and describe how residuals and residual plots indicate if these assumptions may have been violated
- calculate and interpret measures of fit and formulate and evaluate tests of fit and of regression coefficients in a simple linear regression
- describe the use of analysis of variance (ANOVA) in regression analysis, interpret ANOVA results, and calculate and interpret the standard error of estimate in a simple linear regression
- calculate and interpret the predicted value for the dependent variable, and a prediction interval for it, given an estimated linear regression model and a value for the independent variable
- describe different functional forms of simple linear regressions
2.25 PL Credit
If you are a CFA Institute member don’t forget to record Professional Learning (PL) credit from reading this article.