moments, skewness and kurtosis explained

hello guys today's topic is the statistics and under statistics uh the topics which are allotted to me uh moments okay moments unis carcasses and then we'll have combinations and permutations okay so let's finish the first portion the concept part okay and we'll we'll do a bit of sums also first topic allotted to me is moments all right what what are moments here try to understand whenever some data is given to you some frequency distribution is given to you we have to calculate certain measures to understand the characteristics of it all right and these are moments okay i have written here also moments are measures to describe the various characteristics of frequency distribution given to you okay so what do we calculate under moments at the moment we calculate central tendency okay first one is central tendencies all right under central tendencies what do we calculate we calculate mean median mode okay mean is the average value of any distribution given to you median is the middle most or the central value given to you and the mode is the most frequent value which is coming again and again for the frequency distribution all right so i hope central tendencies are there and after that dispersion dispersion is all about how data is dispersed okay you will calculate one central value and from the central value how this data all the observations are distant all right and this is what we basically calculate under dispersions and the most important topic for me as per a syllabus is concerned students and carcasses under moments i'm not going to take care of central tendencies or dispersions all right this will be taken care by other teachers this also will be taken care by some other teacher and my topic for the day is tunis and carter's all right let's see what is skewness as you understood moments are actually calculating or helping you to understand the characteristics or characteristic features for the distribution okay data given to you skewness actually calculates you know helps you whether the data given to you makes a normal curve or not all right i'll put the i'll put the nodes on normal curve okay try to understand normal curve looks like this all right so basically you have left hand side you have right hand side okay and in between you know in between if you if you calculate the data all your mean median mode will lie on same line that's normal all right so skewness helps you to understand whether your data is symmetrical or asymmetrical symmetrical means you'll get a normal curve okay both left hand side and right hand side will be equal all right all your mean median mode will be lying on the same line i hope to hear things okay so basically skewness will tell you whether your data is symmetrical if it is symmetrical it will give you a normal curve with mean median mode all equal so skewness will be zero all right if it is not then it will be asymmetrical in the case of asymmetrical you will have either positively skewed skewness data okay positively skewed data or negatively skewed data all right positively skewed data under positively skewed data if this is your left hand side and this is right inside okay you're putting the data in the curve shape then what will happen you'll see mean will be greater than median and median will be greater than both all right this is how things will be looking like and you will say it is positively skewed all right in the in in other case when your data is skewed towards the right hand side data is skewed towards the right hand side here data is q towards the left hand side so if it is closer towards the right hand side what will happen mean will be less than median okay and median will be less than mode and then in that case you will say that skewness is the the data is negatively skewed or skewness is is will be always in negative value all right i'm repeating once again skewness helps you to calculate and see whether the distribution okay frequency distribution given to you is symmetrical or asymmetrical it will be symmetrical if you're both at left-hand side and right-hand side will be equal okay how will you know you can calculate mean median mode and if all are same that means skewness is zero your data is symmetrical here also you will calculate mean median mode for the data given to you and if it is left okay if it is q towards the left hand side your skewness will be positive if skewness is positive you will if you put it in the in the graphical shape it will be skewed towards the left hand side okay and and similarly if the data is q towards the right hand side you will always get skewness in this in the negative value all right so this is this is skewness we have done all right and the last part okay under moments the last part is consciousness after skin is vertices skewness tells you about whether the data is symmetrical or asymmetrical whether it is left-hand side and right-hand side equal or not or it is key towards left-hand side or right hand side all right under carcasses it talks about the peat okay the peak the peak values okay whether it is peed or not all right peak you understand right you have a hill let's say there is a hill and hill is quite flat so you'll see the peak is not much okay it is quite a quite a quite a flat hill if the hill looks like this then you'll say that the hill has a huge peak all right it has a huge peak so it has a moderate peak or it is quite flat the hill has become very flat let's see okay so basically your data when you put it in the curve shapes okay either it will be very high the peak will be very high or it will be moderate or it will be very flat all right so when you're calculating these things and you're trying to find out the characteristics of data whether it is flat or or it does giving your peak all right in that case you need to calculate carcasses okay and carbon is of three types cartridges are of three types okay first one is leptokartic okay leptokartic what will happen the peak is quite high you can see here under moderate okay under moderately peaked value you'll get a mesocartic all right this is mesocartic it is neither too high nor too flat all right mesocartic and in the third case if you put the data in the curve shape if you're getting quite a flat curve okay very flat curve then in that case you have a bloody garlic all right so with this i can say i have explained i'll take a picture of it i'll put in the group also try to understand and maintain your notes okay notes has to be prepared on your own looking at the video try to understand and then make your notes all right don't just copy okay don't screenshot and then later on copy read okay read from the book whatever books you have watch the video try to understand and then you make your own notes and if you have any questions please ask me okay i'm putting this video in the group the link of this video in the group please try to understand and do ask questions and yes maintain notes on your own

Channel: Sawan singh Raso

Views: 2,856

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Id: cF8tM7SEpZM

Length: 7min 45sec (465 seconds)

Published: Tue Dec 08 2020