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Descriptive Statistics on Companies in the Forest Products Industry

A prerequisite of many statistical methods is that the underlying data material is normally or approximately normally distributed. If the data material violates this assumption, the results of an analysis might be misleading or incorrect. The overall objective with this paper is to provide the reader with descriptive statistics over two separate sets of empirical data and test these sets against the assumption of normality. The data material consists of financial ratios calculated from the international forest products industry. This paper is part of a larger study where we want to analyse the forest products companies'' financial performance world-wide. For that study it is of outmost importance that we know whether the variables in each of our sets of data are normally distributed. Three different test statistics: skewness, kurtosis and Kolmogorov-Smirnov with Lilliefors correction are used to test the data material against normality. Results achieved show that the Kolmogorov-Smirnov test statistic rejected normality for all variables. The skewness ratio for all variables fell within the specified range of approximately normal distributions while half of the kurtosis ratios exceeded the boundaries for approximately normally distributed data.