Multiple Regression

Annette J. Dobson
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Annette J. Dobson: University of Newcastle

Chapter 6 in Introduction to Statistical Modelling, 1983, pp 44-56 from Springer

Abstract: Abstract We begin the discussion of particular generalized linear models by considering the simplest case, (6.1) % MathType!MTEF!2!1!+- % feaaguart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr % 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9 % vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x % fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyEaiabg2 % da9iaadIfacqaHYoGycqGHRaWkcaWGLbaaaa!3C41! $$ y = X\beta + e $$ where (i) y is an N ×1 response vector; (ii) X is an N × p matrix of constants, with linearly independent columns so that rank (X) = p; (iii) ß is a p × 1 vector of parameters; (iv) e is an N × 1 random vector whose elements are independent, identically and Normally distributed ei ~ N(0,σ2) for i = 1, ...,N.

Keywords: Explanatory Variable; Generalize Linear Model; Maximum Likelihood Estimator; Polynomial Regression; Unbiased Estimator (search for similar items in EconPapers)
Date: 1983
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DOI: 10.1007/978-1-4899-3174-0_6

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