location statistics (eg, mean, median, mode, quantiles), dispersion statistics (eg, variance, standard deviation, range, interquartile range), and shape statistics (eg, skewness, kurtosis)”.The descriptive statistics gives the overall description of the data by providing the measures of central tendency, and the measures of dispersion.
statistics. According to Petty( 2015) “A logarithmic price scale is plotted so that the prices in the scale are not positioned equidistantly. instead, the scale is plotted in such a way that two equal percent changes are plotted as the same vertical distance on the scale. The log returns basically is auto – correlated while the case is different with log prices. The log return are usually preferred in quantitative analysis as it gives a better insights in to aspects like normalization and classical statistics. Here the data stream for the 20-year period of January 1995 to December 2014 is used to calculate the log price and log return.
Also a graph representation for the log price and the log returns is formulated in order to deliver a quantitative analysis of the specific data of the company. Here, histogram is used to represent the graph of the statistical data in a visual form
The above analysis explains the descriptive statistics of the log prices and the log return of the data. From the log return, we can observe that the mean value of log return is 5.740008, with a standard deviation of 8.558659. The mean of log prices is 334.934 with the standard deviation of 0.024659. The skewnes of the variable log return is equal to 0.2427 which is a negative value implying that the value of the log return is negatively skewed. The value of kurtosis of the log return is equal to -1.02302 which is a negative value. This implies that the data of log return has a low distributed or low peaked about the mean. The skewnes of the variable log price is equal to -0.