Chapter 5: Linear Regression Analysis

attempts to model the relationship between a dependent variable and independent variable

case where there’s only one independent variable

case where there are two or more independent variable

used when we want to predict the value of a variable based on the value of another variable using one independent variable only

Linear Regression involves in:

analyzing and quantifying systematic risk of an investment relating the return on investment to the return on all risky assets

predicting assumption, spending fixed investments, spending inventory, purchases exports and imports, demand, liquid assets, labor demand and labor supply

observational studies

describing possible relationship between variables

one of the most fundamental supervised machine learning algorithms due to its relative simplicity and well-known properties

the “predictors”

the risk factors

the co-founder

experimenter changes or control

assumed to have a direct effect on the dependent variable

denoted by letter “x”

X

the response factors

denoted by the letter “y”

being tested

dependent on the independent variable

Y

a

b