unit 3 assig 1.docx - Running head HISTORGRAMS AND DESCRIPTIVE STATISTICS Histograms and Descriptive Statistics Capella University Katie Del Rio

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Running head: HISTORGRAMS AND DESCRIPTIVE STATISTICS Histograms and Descriptive Statistics Capella University Katie Del Rio

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HISTOGRAMS AND DESCRIPTIVE STATISTICS2 Part 1: Histograms and Visual Interpretation Skew Skewness is “asymmetry in a statistical distribution, in which the curve appears distorted or skewed either to the left or to the right” (Rajamanickam, 2001, p.58). When looking at the skewness on a graph it will appear as a bell-shaped curve, the mean, average and mode or the maximum point on the curve, are equal (Warner, 2013). There are ways to tell if it has a normal distribution. The tails on the curve (either side) have a mirror image of each other. To tell is the distribution is skewed, the tail on the left is a bit longer than the tail on the right, and the mean is less than the mode (Warner, 2013). In that case it is called negative skewness. And when the
HISTOGRAMS AND DESCRIPTIVE STATISTICS3 distribution is skewed towards the right the tail on the right side of the curve is longer than the left side. In that case it means that the mean is greater than the mode. This is called positive skewness. The chart that is being used with the information provided for our current class the skewness of the curve is considered positive, because the right side is longer than the right side. Statistics gender N Valid 105 Missing 0 Skewness .456 Std. Error of Skewness .236

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HISTOGRAMS AND DESCRIPTIVE STATISTICS4 Kurtosis Kurtosis is the statistical measure in which the distribution, or the skewness, of observed data around the mean, sometimes referred to as the volatility or volatility. This is used in statistics in references to the graphs and charts. “Kurtosis can be present in a chart with fat tails and a low, even distribution, as well as be present in a chart with skinny tails and a distribution concentrated toward the mean” (DeCarlo, 1997). When looking at kurtosis it is important to understand that it is “a measure that describes the shape of the distribution’s tail in relation to its overall shape” (DeCarlo, 1997) rather than the peak of the distribution.
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