INSTRUCTOR MANUAL
Instructor’s Manual for Principles of Finance
03/21/22 1
, Instructor’s Manual for Principles of Finance
Chapter 14
Regression Analysis in Finance
Chapter Summary
This chapter examines the applications of correlation and regression analysis in the finance
industry. Correlation analysis allows the determination of a statistical relationship between two
numeric quantities. Regression analysis can be used to predict one quantity based on a second
quantity, assuming there is a significant correlation between the two quantities. For example, in
finance, regression analysis is used to calculate the beta coefficient of a stock, which represents
the volatility of the stock versus overall market volatility, with volatility being a measure of risk.
In this discussion, the focus will be on analyzing the relationship between one dependent
variable and one independent variable, where the relationship can be modeled using a linear
equation. This type of analysis is called linear regression.
Lecture Outline
14.1 Correlation Analysis
Correlation analysis studies the relationship between bivariate data, which is data collected on
two variables where the data values are paired with one another. Correlation is the measure of
association between two numeric variables. For example, you may be interested to know if
there is a correlation between bond prices and interest rates or between the age of a car and
the value of the car. To investigate the correlation between two numeric quantities, the first
step is to create a scatter plot that will graph the (x, y) ordered pairs. The independent, or
explanatory, quantity is labeled as the x-variable, and the dependent, or response, quantity is
labeled as the y-variable.
LO 1: Calculate a correlation coefficient.
To assess linear correlation, the graphical trend of the data points is examined on the scatter
plot to determine if a straight-line pattern exists. If a linear pattern exists, the correlation may
indicate either a positive or a negative correlation. A positive correlation indicates that as the
independent variable increases, the dependent variable tends to increase as well, or as the
independent variable decreases, the dependent variable tends to decrease (the two quantities
move in the same direction). A negative correlation indicates that as the independent variable
increases, the dependent variable decreases, or as the independent variable decreases, the
dependent variable increases (the two quantities move in opposite directions). If there is no
relationship or association between the two quantities—where one quantity changing does not
affect the other quantity—you conclude that there is no correlation between the two variables.
When inspecting a scatter plot, it may be difficult to assess a correlation based on a visual
inspection of the graph alone. A more precise assessment of the correlation between the two
quantities can be obtained by calculating the numeric correlation coefficient (referred to using
the symbol r).
03/21/22 2
Instructor’s Manual for Principles of Finance
03/21/22 1
, Instructor’s Manual for Principles of Finance
Chapter 14
Regression Analysis in Finance
Chapter Summary
This chapter examines the applications of correlation and regression analysis in the finance
industry. Correlation analysis allows the determination of a statistical relationship between two
numeric quantities. Regression analysis can be used to predict one quantity based on a second
quantity, assuming there is a significant correlation between the two quantities. For example, in
finance, regression analysis is used to calculate the beta coefficient of a stock, which represents
the volatility of the stock versus overall market volatility, with volatility being a measure of risk.
In this discussion, the focus will be on analyzing the relationship between one dependent
variable and one independent variable, where the relationship can be modeled using a linear
equation. This type of analysis is called linear regression.
Lecture Outline
14.1 Correlation Analysis
Correlation analysis studies the relationship between bivariate data, which is data collected on
two variables where the data values are paired with one another. Correlation is the measure of
association between two numeric variables. For example, you may be interested to know if
there is a correlation between bond prices and interest rates or between the age of a car and
the value of the car. To investigate the correlation between two numeric quantities, the first
step is to create a scatter plot that will graph the (x, y) ordered pairs. The independent, or
explanatory, quantity is labeled as the x-variable, and the dependent, or response, quantity is
labeled as the y-variable.
LO 1: Calculate a correlation coefficient.
To assess linear correlation, the graphical trend of the data points is examined on the scatter
plot to determine if a straight-line pattern exists. If a linear pattern exists, the correlation may
indicate either a positive or a negative correlation. A positive correlation indicates that as the
independent variable increases, the dependent variable tends to increase as well, or as the
independent variable decreases, the dependent variable tends to decrease (the two quantities
move in the same direction). A negative correlation indicates that as the independent variable
increases, the dependent variable decreases, or as the independent variable decreases, the
dependent variable increases (the two quantities move in opposite directions). If there is no
relationship or association between the two quantities—where one quantity changing does not
affect the other quantity—you conclude that there is no correlation between the two variables.
When inspecting a scatter plot, it may be difficult to assess a correlation based on a visual
inspection of the graph alone. A more precise assessment of the correlation between the two
quantities can be obtained by calculating the numeric correlation coefficient (referred to using
the symbol r).
03/21/22 2
