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Simple Linear Regression
Regression expresses a relation used to predict one variable, called the response variable (or \dependent" variable
{ often called y), from other variables, called predictors (or \independent" variables { often called x1;x2;:::;xk ), and
provides us with an equation to make this prediction. The regression equation that we calculate is descriptive of the
sample (like sample mean, like sample standard deviation); we use various inference methods to see the extent to which
we believe the description carries over to the population from which the sample is drawn.
A sample question
This table represent a sample of ten trucks; for each, we have with the age in years and the annual maintenance cost. We
want to nd a linear equation, using this information, which most closely describes (\predicts") the [average] maintenance
cost of a truck, based on its age. [Notice how the language tells us the 'predictor" is age and the 'response" (\predicted")
is cost.
Truck age cost
number (yea...

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