 Hello and welcome to the session. In this session we interpret differences in shape, center and spread in the context of the data sets accounting for possible effects of extreme data points i.e. outliers. Now in our earlier session we had discussed about the shape of distribution. We know about various measures of central currency, measures of spread and outliers. First of all let us recall center. Now measure of center refers to the measure used to describe the most typical value in the data set. This value represents the very data most common ratio of center is mean and medium. Now let us discuss about spread. Now spread of distribution, the variability of data is of plus or minus the central value then the spread is smaller. However the observations fall from the center. The greater the variability of spread of the data and we use the total range measures of spread. Now the shape of distribution is described by symmetry. Number of peak skewness solutions are corrected values that differ greatly from the other observations and these extreme values in the distribution are called outliers. The values are even very large always small the values in the data set. The value is considered as an outlier if it is at least 1.5 times in the patine range of a time that is the value is considered to be an outlier. It lies outside the specific range that is q1 minus 1.5 into inter-partial range with q3 plus 1.5 into inter-partial range. Now in this terms the bar of the outlier will be far away from the other bars in the display. Now when we compare two data sets one should discuss each characteristic of the data that is center, spread, shape and outlier. Now let us discuss an example to study black pier population. The one length department measures the length of 143 meters. The following dot plots show the distribution of lengths of nail and we want to compare the two distributions. Now we have given one dot plot showing the length of 100 nail pier's the shape of distribution. Now the first distribution is very slightly skewed to the left distribution is skewed. You can see that this distribution is skewed. Now let us see the center. Now here the length of nail pier's is concentrated between 58 its median. Then it will be in pier is around. Now in second distribution we see that most data that is most of the data is concentrated between is around 59. Now let us discuss spread of the distribution. Now in distribution round we can see that data is moving further to distribution the data on the center and is less variation in nail data set will be greater than female data is more spread in nail distribution set. Now let us discuss outliers. There are no outliers. There is no value in we can see that the value 89 is lying very far away from all other values in the data set of nail pier's. Then that of female pier's which length of nail pier is equal to average length of female pier. We have discussed just using shape, spread, center and outliers. And this completes our session. Hope you all have enjoyed the session.