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- W1996431560 abstract "This paper addresses the problem of detecting a mixture of parallel regression lines when information about group member¬ship of individual cases is not given. The problem is approached as a missing variable problem, with the missing variables being the dummy variables that code for groups. If a mixture of par¬allel regression lines with normally distributed error terms is present, a simple regression model without dummy variables will produce residuals that follow approximately a mixed normal dis¬tribution. In a simulation studyr several goodness-of-fit tests of normality were used to test the residuals obtained from mis-specified models that excluded dummy variables, Factors varied in the simulation included the number and the separation of the parallel lines and the sample size, The goodness-of-fit test based on the sample kurtosis (82) was overall most powerful in detecting mixtures of parallel regression lines, Applications are discussed." @default.
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- W1996431560 date "1987-01-01" @default.
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- W1996431560 title "Comparative performance of tests of normality in detecting mixtures of parallel regression lines" @default.
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- W1996431560 doi "https://doi.org/10.1080/03610928708829522" @default.
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