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linear regression


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The simplest and most used of all statistical regression models. The model states that the random variable Y is related to the variable x by

Y=α+βx+ε,

where the parameters α and β correspond to the intercept and the slope of the line, respectively, and ε denotes a random error. With observations (x1, y1), (x2, y2),…, (xn, yn) the usual assumption is that the random errors are independent observations from a normal distribution with mean 0 and variance σ2. In this case the parameters are usually estimated using ordinary least squares. The estimates, denoted by α̂ and β̂, are given by , where and ȳ are the means of x1, x2,…, xn and y1, y2,…, yn, respectively, and where . The variance σ2 is estimated by , where .

A 100 (1-θ)% confidence interval for β is provided by , where tν(θ) is the upper 100θ% point (see percentage point) of a t-distribution with ν degrees of freedom. A 100(1−θ)% confidence interval for the expected value of Y when x=x0 is . A 100(1-θ)% prediction interval for the value y0 of Y when x=x0 is See also multiple regression model; regression diagnostics; regression through the origin.

Subjects: Probability and Statistics — Social Sciences.


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