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In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
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The normal equations can be derived directly from a matrix representation of the problem as follows. The objective is to minimize = ‖ ‖ = () = +.Here () = has the dimension 1x1 (the number of columns of ), so it is a scalar and equal to its own transpose, hence = and the quantity to minimize becomes
The first OLS was the mirror landing aid, one of several British inventions made after the Second World War revolutionising the design of aircraft carriers. The others were the steam catapult and the angled flight deck. The mirror landing aid was invented by Nicholas Goodhart. [2]
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This data set gives average masses for women as a function of their height in a sample of American women of age 30–39. Although the OLS article argues that it would be more appropriate to run a quadratic regression for this data, the simple linear regression model is applied here instead.
OLS or Ols may refer to: Oleśnica (German: Öls), Poland; Operation Lone Star, Texas border operation; Optical landing system, U.S. Navy landing system; Order of Luthuli in Silver, a South African honour; Ordinary least squares, a method used in regression analysis for estimating linear models
The two regression lines are those estimated by ordinary least squares (OLS) and by robust MM-estimation. The analysis was performed in R using software made available by Venables and Ripley (2002). The two regression lines appear to be very similar (and this is not unusual in a data set of this size).