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- W4238351502 abstract "This chapter discusses the use of weighted least squares (WLS) as a strategy to correct the lack of homoscedasticity in the errors. It presents two methods to achieve homogeneity of variance: the basic assumption for estimating the parameters of a multiple linear regression model using the ordinary least squares (OLS) method. Situations that can cause deviation from variance homogeneity include different number of observations for each value of X, and increase in the variance of Y as X increases. Homogeneity of variance is an assumption required to justify the use of t- and F-tests when performing inference on the coefficients of a linear regression model. In designed experiments with large numbers of replicates, weights can be estimated directly from sample variances of the response variable for each combination of the predictor variables." @default.
- W4238351502 created "2022-05-12" @default.
- W4238351502 date "2017-02-11" @default.
- W4238351502 modified "2023-10-14" @default.
- W4238351502 title "Weighted Least‐Squares Linear Regression" @default.
- W4238351502 doi "https://doi.org/10.1002/9781119212515.ch8" @default.
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