There are two modes! that is one of the main weaknesses of the mode as a measure of central tendency.
If you have a set of individual observations, the mode is the observation that occurs the most often.However, with very large sets, you may wish to group the data into classes. In that case, the class with the largest frequency is the modal class.The modal class need not contain the mode. Also, the modal class depends on how the classes are defined.
It is quite possible for there to be no mode. A sample from a continuous distribution may have a modal class but is very unlikely to have a mode.
Both classes are modal classes.
When data is grouped and each of the intervals or categories has the same relative frequency, then no mode can be calculated. This can happen when the dataset is very limited. If all numbers in a dataset are the same, then it is impossible to calculate a mode, no matter how the data is grouped. Sometimes the level of variation is so much less than our measurement capability that we can not detect variations in variables.
Mode.
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
It is simply a distribution which has two modal classes: you cannot convert two of them into a mode.
If you have a set of individual observations, the mode is the observation that occurs the most often.However, with very large sets, you may wish to group the data into classes. In that case, the class with the largest frequency is the modal class.The modal class need not contain the mode. Also, the modal class depends on how the classes are defined.
It is quite possible for there to be no mode. A sample from a continuous distribution may have a modal class but is very unlikely to have a mode.
Both classes are modal classes.
Mode is the most frequent value in a dataset. It is a measure of central tendency along with mean and median. Mode is useful for representing the typical value or category in a dataset.
When data is grouped and each of the intervals or categories has the same relative frequency, then no mode can be calculated. This can happen when the dataset is very limited. If all numbers in a dataset are the same, then it is impossible to calculate a mode, no matter how the data is grouped. Sometimes the level of variation is so much less than our measurement capability that we can not detect variations in variables.
The Mode: The mode is defined as the most frequently observed value. For grouped data, the mode is the most commonly observed category, and for ungrouped data, the mode is the value which occurs most frequently. For a grouped frequency distribution, the mode is given by Mo = l1 + ((f1 - f0)/(2f1 - f0 - f2))*(l2 - l1) where: l1 - l2= the modal class f1= frequency of the modal class f2= Frequency of the class following the modal class f0= Frequency of the class following the modal class Using the same example for computation of the mode, the modal class is identified as the mean of the category and is used for the responses in the computation of the mode. CumulativeX Freq Frequency (Less Than)---------------------------------------------Age 11-13 10 10Group 14-16 17 2717-19 23 50
Then the collected data is bi-modal
mode
It is often claimed that height is bi-modal because there will be one modal height for men and one for women. But unless there are exactly the same number of men and of women in the modal class, both cannot be modes. Consequently, this attribute really has only one mode. The same applies to other characteristics.
Mode.