by getting the mean and multiply by the median
True-as in an average.
Mode.
The mean.
The mode.
The variable, height, is a continuous variable. The mode is not a good measure of central tendency for continuous variables because you would need a very large number of observations (pupils) before you are likely to get a useful number of repeat values. The modal class may be a good measure. Provided you do not have extremely short or extremely tall pupils, the mean would probably be the best.
The mode.
If you want to ask questions about "this situation", then I suggest that you make sure that there is some information about the situation in the question.
The median.
No, correlation is not a measure of central tendency. It is a statistical measure that describes the strength and direction of a relationship between two variables. Measures of central tendency, such as mean, median, and mode, summarize data by identifying a central point within a dataset. In contrast, correlation focuses on how two variables move in relation to each other.
True-as in an average.
The mean may be a good measure but not if the data distribution is very skewed.
For which measure of central tendency will the sum of the deviations always be zero?
no it is a measure of dispersion.
It describes the "middle" of the data set.It describes the "middle" of the data set.It describes the "middle" of the data set.It describes the "middle" of the data set.
Measures of central tendency are averages. Range , the difference between the maximum and the minimum, is a measure of dispersion or variation.
Mode is the only measure of central tendency to measure quantitative dataor qualitative data.
The measure that describes the center of data is known as the "central tendency." The three most common measures of central tendency are the mean, median, and mode. The mean is the average of all data points, the median is the middle value when data is ordered, and the mode is the most frequently occurring value. Each measure provides different insights into the data's distribution and can be used depending on the context.