Week 2 workshop Assignment Example | Topics and Well Written Essays - 500 words

Week 2 workshop - Assignment Example The graph is now positively skewed from mean as shown in the diagram below: The appropriate measures of central tendency for skewed distribution are either mean or median. Median is a more appropriate measure for highly skewed distributions. The skewness of the graph that we have plotted is not very high. Hence the best measure of central tendency is the mean. The average salary of the corporate customers is now $345,455. Gender is an example of nominal data. It can take either of the two values: Male or Female. Mode which is the value which has the highest frequency in the given data set is the best measure of central tendency in this case (QuickMBA, 2010). The mode of the distribution will tell us whether the data has more males or more females which is an appropriate measure for central tendency. The three key different measures of central tendency are Mean, Median and Mode. All the three are not the best measures for all types of distribution. They have different values for differently skewed distributions. For a symmetrical distribution, all the three measures converge on the same value. Mean is the most sensitive measure to skewness (Levin and Rubin, 2009). In case the distribution contains outlier values which impact the skewness of the distribution positively or negatively, the mean moves from being the highest (positively skewed distribution) to being the lowest (negatively skewed distribution). In any distribution, median is always in the middle of mean and mode. Median is the least sensitive measure for skewed distributions. Standard deviation and variance are both the measures of dispersion. Standard deviation is the square root of variance. Variance is the average value of the squared difference of the each value and the mean. Since variance is calculated after squaring, all the differences take positive value. Moreover, larger differences get more weight and are more suitable in cases where there are more outliers. Standard