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Hutcheson, G. & Hutcheson, G. (2011). Ordinary least-squares regression. In L. Moutinho & G. Hutcheson (Eds.), The SAGE dictionary of quantitative management research (pp. 225-228). London: SAGE Publications Ltd doi: 10.4135/9781446251119.n67

Hutcheson, Graeme and Graeme Hutcheson. "Ordinary Least-Squares Regression." In The SAGE Dictionary of Quantitative Management Research, edited by Luiz Moutinho and Graeme Hutcheson, 225-228. London: SAGE Publications Ltd, 2011. doi: 10.4135/9781446251119.n67.

Hutcheson, G & Hutcheson, G 2011, 'Ordinary least-squares regression', in Moutinho, L & Hutcheson, G (eds), The sage dictionary of quantitative management research, SAGE Publications Ltd, London, pp. 225-228, viewed 11 December 2019, doi: 10.4135/9781446251119.n67.

Hutcheson, Graeme and Graeme Hutcheson. "Ordinary Least-Squares Regression." The SAGE Dictionary of Quantitative Management Research. Eds. Luiz Moutinho and Graeme Hutcheson. London: SAGE Publications Ltd, 2011. 225-228. SAGE Knowledge. Web. 11 Dec. 2019, doi: 10.4135/9781446251119.n67.

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Ordinary Least-Squares Regression
Ordinary least-squares regression
GraemeHutcheson
Introduction

Ordinary least-squares (OLS) regression is a generalized linear modelling technique that may be used to model a single response variable which has been recorded on at least an interval scale. The technique may be applied to single or multiple explanatory variables and also categorical explanatory variables that have been appropriately coded.

Key Features

At a very basic level, the relationship between a continuous response variable (Y) and a continuous explanatory variable (X) may be represented using a line of best fit, where Y is predicted, at least to some extent, by X. If this relationship is linear, it may be appropriately represented mathematically using the straight line equation ‘Y = α + βx’, as shown in Figure 58 (this line was computed using ...

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