What is Linear Regression?
Linear regression is used to make predictions about a single value. Simple linear regression involves discovering the equation for a line that most nearly fits the given data. That linear equation is then used to predict values for the data.
For instance, when NASA scientists wish to put the adjacent satellite into space, they make assumptions based on available data. Then they plan to place the satellite in an exact position above the earth with calculations based on those assumptions.
Some linear regression examples
Example 1: A cost modeller wants to know find the prospective cost for a new contract based on the data collected from previous contracts.
Example 2: If the university authorities want to predict a student's grade on a freshman college calculus midterm based on his/her SAT score, then they may apply linear regression.
What is a linear regression equation?
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A regression equation is a mathematical equation that can be used
to predict the values of one dependent variable from known values of one
or more independent variables. The term is derived from the heredity
studies performed by Sir Francis Galton in which he compared the heights
of sons to the height of their fathers.
Galton showed that the height
of the sons of tall fathers regressed towards the mean height of the population through several successive generations. In other words, sons of unusually tall fathers tend to be shorter than their fathers and sons of unusually short fathers tend to be taller than their fathers. Today, the term regression applies to different types of prediction problems and does not necessarily imply a regression towards the population mean.