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Generally in Statistics, Kurtosis is associated with the degree to which the peak of any distribution gets pointed. Kurtosis is explained in details by Kurtosis Assignment Help. With comparison to another symmetrical or normal distribution, the extent to which any peak of some distribution end is measured by Kurtosis. It is important in understanding distribution in case of central tendency. There may be distributions having similar means and standard deviation differing from one another widely. On the premise of 2nd and 4th central moments, Kurtosis is defined. Mesokurtic is the flat or peak of that will be found in case of normal curve. Leptokurtic is defined as the curve that comes in contact with the great peak as compared to normal one. Platykurtic are the curves which are flatter than the normal curves. In other words, Kurtosis Assignment Help says that kurtosis is all about the middle portion of the frequency curve. It can be said that, the leptokurtic curve is more peaked than the normal curve. Leptokurtic curve has higher tails at its base but narrower at its centre. The mesokurtic curve has a normal characteristic of neither too peaked nor too flat. It has neither too long nor too short tail on its base and occupies a normal position in the centre. But the platykurtic curve is more flat – topped as compared to the normal one having a short tail on its base and broader space in the centre.
Methods of Arranging and Summarizing Data in Kurtosis
In Kurtosis, the elements of the data can be arranged and summarized in the manner which is given in Kurtosis Assignment Help
1. Data Editing
2. Managing Missing and Outlines
3. Data Coding
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Missing values and Outlines is described in Kurtosis Assignment Help as below
Here in this case, the researchers couldn’t collect the data which they have planned to collect or the data are lost due to some or the other reasons. When the data set has more than one missing values, many statistical tools cannot be applied. Therefore, great care should be taken by the researchers in order to avoid the problems related to missing values. The main way to avoid the missing values is either to leave the observation or to replace the missing values with the arithmetic mean of other collected observations.
In case of outliners, the observations are quite different from the other set of observation in the data set. Here, all the statistical techniques can be employed when there is an outline in the data set but the disadvantage of this is their interpretations may be misleading. In order to manage the missing and inconsistent information, data cleaning is required.
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