 अरेंकाया आंगा क्या है किता सुदता होगी musicians ूई धश्वाह आप आब अज्केँए और थिसकरही घरह आद आद आप आप आते इस्ग है। अच्चर दवीत आद आप क्या आप आप आप आप आप आप आप आप आप आप आप आप आना। आप आप आपis that अर वेडिएख़ धब पर उनको लेयाएंगे क्ता फ़त आंगांका इंट्रपेटेशन कर पाएंगे और ताब उस अंट्रेपेटेशन का कोई अर्ठोगा तो नद्रद स्वोर है क्या? नद्रद स्वोर is a derived score, it has a specified or fixed mean and fixed standard deviation. how many standard deviations of this score lie below or above the mean of distribution i.e. the mean of distribution how many deviations we lie above or below this is what z score tells us what else it tells us is a standard score which has a mean of 0 and standard deviation of 1 because we have told that your standard score will be fixed and mean will be fixed and standard deviation will be fixed and mean 0 will be fixed and standard deviation 1 will be fixed it is computed by z is equal to x minus m upon sd where x is the raw score m is the mean of the distribution and sd is the standard deviation so if we know the raw score mean and standard deviation then we can take out z okay merit is that z score represents the most precise way of indicating position in distribution i.e. within we can tell the position exactly because of z score when we use z score and percentile rank and other guests of within this is better it's computation it's demerit demerit is the value of minus because we are saying the mean of distribution is the top and the bottom so when the top values come the minus point then we talk about normalised standard score when we did linear transformation then we got linear transformed standard score and when we do normalised transformation then the standard score will be normalised standard score so why do we need normalisation that one general demerit of linearly transformed standard score is that they can be compared only when they have been derived from distributions that have approximately the same form i.e. when they have been derived from distributions that have been approximately the same form i.e. the two things that are different when they can be compared then the linear transformed standard score will work but if a distribution is skewed which is more or less this type of distribution i.e. a distribution is skewed and a distribution is following a normal distribution curve then the two standard scores cannot be compared then we will not be able to compare the two standard scores then we will have to normalise the two standard scores derived from different types of distribution becomes necessary a direct solution is to convert raw scores into standard scores which are normalised i.e. when we normalise the two standard scores then we will be able to compare the two standard scores because if a standard score was skewed then we normalised both so we will not be able to compare so what are the standard scores and what are the names of the standard scores and what are the names of the standard scores t-scores, 10-9-scores and the deviation i.e. t-scores are most popular and t-scores can also be converted to z-scores you will get a question that if you have given t-scores then you are asking the value of z-scores or if you have given z-scores then you are asking t-scores it is very easy to convert t-scores to z-scores this is the formula i.e. z-scores are 10-9-scores and 50-9-scores are not connected because the mean of t-scores is 50-9-scores and standard deviation is 10-9-scores i.e. t-scores are equal to z-scores plus m-scores so what is t-scores is defined as a standard score which is based upon the mean of 50 and standard deviation of 10-10-scores i.e. we have said that whenever we talk about standard scores then the mean of t-scores is 50-9-scores and standard deviation is 10-9-scores so this is done and what are we saying that when we follow z-scores then the range of t-scores is minus 3 to plus 3 i.e. we have considered 0 as the mean so from 0 to 3 there are 3 benefits so 3 standard deviation is here and 3 benefits are below i.e. minus 3 to plus 3 the range of t-scores is z-scores and this range is 20-80 most distributions i.e. in many distributions and if we distribute it then automatically it will take the shape of normal curve and you can change t-scores to z-scores t-scores is equal to z-scores plus m-scores which is 10-10-scores and m-scores is 50-10-scores so we have taken this now what is 10-9-scores standard 9-scores standard 9-scores standard 9 so this is a contraction of standard 9 standard 9-scores and it has expressed in digits ranging from 1 to 9 from 1 to 9 the range of z-scores is minus 3 to plus 3 the range of t-scores is 20-80 the range of t-scores is 1 to 9 and we have to remember that z-scores is 0 in standard 9-scores the range of t-scores is 50-10-scores and in standard 9-scores the range of t-scores is 5 and standard 9-scores is 1.96 so when raw-scores are transformed into standard 9-scores they automatically take the shape approximating the normal curve in t-scores as a matter of fact standard 9-scores is 10 0-10 so 10-10 so the midpoint is 5 so 5 is the mean but we condensed the C-scores means 0-10 is 0 we removed it and we pressed it so how much left is 1-9 so that is what your standard 9-scores is as a matter of fact standard 9-scores are the condensed scores how 11-scores are 11-scores how 11-scores are 10-scores and when 11-scores are 11-scores we can take the mean of 5 with the mean lying exactly at 5 and then we condensed 0-10 so where is our scale from 1-9 so its mean is 5 and standard deviation is 1.96 so the deviation IQ is the first test then we condensed the C-scores in terms of the deviation IQ was the bachelor intelligence scale when we read the intelligence the concept of intelligence when we read the measurement of intelligence we read the bachelor the adult intelligence scale so they used the deviation IQ and they told us that when we talk about standards the mean is fixed and standard deviation is fixed so here the mean is fixed so most of the intelligence tests use the deviation IQ score rather than the ratio IQ score so mean is 100 and SD is 15 okay so if you are asking about standard score or C-scores or Z-scores then tell us that you cannot interpret from raw score so you need standard score then there are two ways to change the standard score linear transformation or normalized transformation then when you change the linear transformation then the standard score is Z and Z is equal to X minus M upon SD i.e. we will remove the mean from raw score and we will run away from SD then why did you need normalized score because one distribution was skewed and the other was normalized so we had to normalize for comparison so when we normalized so we read the three main scores T is equal to Z into 10 plus 50 this is the formula then in standard 9 and on C scale which was 0 to 10 were 11 points we condensed it so 1 to 9 is left so standard 9 is called as standard 9 and the deviation IQ is mean is 100 and SD is 15 SD is 15 means if we will go one step ahead then mean is 100 if we go one step ahead then mean is 150 and if we go one step below then mean is 150 i.e. minus 1 to plus 1 then mean is 150 to 115 we will vary okay now try to understand this i have tried to explain to you done