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• W134073044 abstract "The Central Limit Theorem and other limit theorems of statistics provide the basis for the centrality of the normal distribution in statistical inference. These theorems tell us that, regardless of the underlying distribution of the data, as the sample size on which they are based gets very large the sampling distribution of statistics calculated from the sample becomes closer to that of some normally distributed variable. But there are many inferential situations which require more than merely the normality of the sampling distribution of a statistic; they require normality of the data distribution itself. For example, standard inference about regression parameters requires that the residuals are normally distributed. In this chapter we describe methods for testing for the normality of the distribution of data. In a subsequent chapter we will describe methods for transforming data which fail the normality test so that the transformed data has a normal distribution." @default.
• W134073044 created "2016-06-24" @default.
• W134073044 creator A5042443312 @default.
• W134073044 date "1988-01-01" @default.
• W134073044 modified "2023-09-28" @default.
• W134073044 title "Testing for Normality" @default.
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• W134073044 doi "https://doi.org/10.1007/978-1-4612-3794-5_2" @default.
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