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Can all quartiles and deciles be expressed in percentiles?
Isisdeanne
yes! Since Q1 = P25
Q2=P50
Q3=P75
Similarly,
D1=P10
D2=P20
.
.
.
D9=P90
Where,
Q1, Q2, Q3 are lower, median and upper quartiles respectively
Dn are the deciles
Good Luck~!
Sehrish
Wiki User
∙ 14y ago
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Why is it only possible to estimate the mean median and quartiles?
In reality, a statistician never really has ALL the data. The data is instead taken from a sample of the whole population. If this sample is representative of the entire population, then any statistics based on the sample should be good estimates of the whole but probably not a perfect match. Of course the more data you get from the whole population the better the estimate, but it will always be an estimate unless you census the enitire population.
How do you find percentiles when given standard deviation and mean?
Each standard deviation represents a certain percentile. So if we use two decimal places, −3 is the 0.13th percentile, −2 the 2.28th percentile, −1 the 15.87th percentile, 0 the 50th percentile , +1 the 84.13th percentile, +2 the 97.72th percentile, and +3 the 99.87th percentile.The mean, median and mode are all the same it the distribution is normal.BUT WHY DOES THIS WORK? HOW DO YOU DO IT?The main idea to make all this work and understandable is that the area under the normal curve is one. So if you have a SD and a mean, you can find the z score.Then, using a calculator, or a table, or even sometimes just some rules you may have learned like the empirical rule, you can find the area to the left or right of any given z score. This area is actually a percentile!So for example, if convert a data point to a z - score using the mean and standard deviation ( The formula is z=(x-mean)/standard deviation, by the way), and I look up the probability of that z-score, and say it is .25. Then it is the 25th percentile.The table below gives you all the percentiles and their corresponding z scores.z-score percentile for normal distributionPercentilez-ScorePercentilez-ScorePercentilez-Score1-2.32634-0.412670.442-2.05435-0.385680.4683-1.88136-0.358690.4964-1.75137-0.332700.5245-1.64538-0.305710.5536-1.55539-0.279720.5837-1.47640-0.253730.6138-1.40541-0.228740.6439-1.34142-0.202750.67410-1.28243-0.176760.70611-1.22744-0.151770.73912-1.17545-0.126780.77213-1.12646-0.1790.80614-1.0847-0.075800.84215-1.03648-0.05810.87816-0.99449-0.025820.91517-0.954500830.95418-0.915510.025840.99419-0.878520.05851.03620-0.842530.075861.0821-0.806540.1871.12622-0.772550.126881.17523-0.739560.151891.22724-0.706570.176901.28225-0.674580.202911.34126-0.643590.228921.40527-0.613600.253931.47628-0.583610.279941.55529-0.553620.305951.64530-0.524630.332961.75131-0.496640.358971.88132-0.468650.385982.05433-0.44660.412992.326
Why use standard deviation In what situations is it special?
I will restate your question as "Why are the mean and standard deviation of a sample so frequently calculated?". The standard deviation is a measure of the dispersion of the data. It certainly is not the only measure, as the range of a dataset is also a measure of dispersion and is more easily calculated. Similarly, some prefer a plot of the quartiles of the data, again to show data dispersal.t Standard deviation and the mean are needed when we want to infer certain information about the population such as confidence limits from a sample. These statistics are also used in establishing the size of the sample we need to take to improve our estimates of the population. Finally, these statistics enable us to test hypothesis with a certain degree of certainty based on our data. All this stems from the concept that there is a theoretical sampling distribution for the statistics we calculate, such as a proportion, mean or standard deviation. In general, the mean or proportion has either a normal or t distribution. Finally, the measures of dispersion will only be valid, be it range, quantiles or standard deviation, require observations which are independent of each other. This is the basis of random sampling.
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