They are both modal classes - the distribution is bi-modal.
If there are more than one class intervals which have the same frequency (equally qualifying to be the mode class) then both of the classes will be the mode class. this is called bimodal. However to calculate the mode of grouped data use the following formula Mode = L + [ (F - F1) / { (F - F1) + (F - F2) } ] * h where L = Lower limit of the modal class F = Frequency of the modal class F1 = Frequency of the class immediately previous of modal class F2 = Frequency of the class immediate next of modal class h = Range of the modal class (higher limit - lower limit) this is what i found out after reading books and understanding them. Please correct me if i am wrong. Thanks, Salman Ahmad
The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.
The distribution is bimodal. That is all there is to it.
The mode has two or more bars on the graph with the same height.
Both classes are modal classes.
Then the collected data is bi-modal
There are two modes! that is one of the main weaknesses of the mode as a measure of central tendency.
They are both modal classes - the distribution is bi-modal.
If there are more than one class intervals which have the same frequency (equally qualifying to be the mode class) then both of the classes will be the mode class. this is called bimodal. However to calculate the mode of grouped data use the following formula Mode = L + [ (F - F1) / { (F - F1) + (F - F2) } ] * h where L = Lower limit of the modal class F = Frequency of the modal class F1 = Frequency of the class immediately previous of modal class F2 = Frequency of the class immediate next of modal class h = Range of the modal class (higher limit - lower limit) this is what i found out after reading books and understanding them. Please correct me if i am wrong. Thanks, Salman Ahmad
The distribution is bi-modal. That is to say both the numbers are modes.
Technically, every number that appear once is the mode (multiple modes). In practice however, it is best to not use the mode in this situation. If you divide the data in classes, one of the classes will be the mode of the new variable.
ose and modal are two different fabric
They you have two modal lengths. It is quite possible to have none, one or many modes.
A bi-modal data set is a data set that has two modes. In the data set 1, 2, 2, 3, 4, 4, 5 the mode is 2 AND 4. So it is a bi-modal data set. Hope that helps.
The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.The mode can be used with both kinds of data. The median may be used with ordinal data but great care is required if the median falls between two classes of observations.
unimodal in movement of materials or products only one transportation system is used whereas multimodal transportation two and above transportation mode involve