Yes, quantitative data sets can have medians. The median is a measure of central tendency that represents the middle value of a data set when it is ordered from least to greatest. If the data set has an odd number of observations, the median is the middle value; if it has an even number, the median is the average of the two middle values. Thus, medians are applicable and useful for summarizing quantitative data.
The answer depends on what sort of variables the data are (qualitative, quantitative-discrete, quantitative-continuous are; the nature of the relationship (if any) between the data sets; how much information you wish the graph to convey and how much you would prefer to describe in the accompanying text.
Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.
If the skewness is different, then the data sets are different.Incidentally, there is one [largely obsolete] definition of skewness which is in terms of the mean and median. Under that definition, it would be impossible for two data sets to have equal means and equal medians but opposite skewness.
Go halfway between the two. If your two medians are, say, 27 and 29, the median is 28 :)
Any kind of graph can be used for quantitative data.
The answer depends on what sort of variables the data are (qualitative, quantitative-discrete, quantitative-continuous are; the nature of the relationship (if any) between the data sets; how much information you wish the graph to convey and how much you would prefer to describe in the accompanying text.
Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.Any data consisting of two sets of quantitative measures on a set of objects. Although the horizontal axis is often used for categories, the graph is then not a Cartesian graph.
don't you mean quantitative data and qualitative data?
quantitative data is the characteristics obtained from an experiment usually the best way to collect quantitative data is to observe your subject.
quantitative data is the characteristics obtained from an experiment usually the best way to collect quantitative data is to observe your subject.
If the skewness is different, then the data sets are different.Incidentally, there is one [largely obsolete] definition of skewness which is in terms of the mean and median. Under that definition, it would be impossible for two data sets to have equal means and equal medians but opposite skewness.
Numerical data is quantitative research
The length of a movie is quantitative data.
Go halfway between the two. If your two medians are, say, 27 and 29, the median is 28 :)
Quantitative data is quantity - how much. Qualitative data is quality - is it good? what is it like?
Any kind of graph can be used for quantitative data.
To compare two data sets displayed in box plots, you can analyze their medians, which indicate the central tendency of each data set. Additionally, examine the interquartile ranges (IQRs) to assess the spread and variability, as a larger IQR suggests more dispersion in the data. It's also important to look for overlap between the box plots, which can indicate similarity or differences in data distributions. Finally, consider any outliers that may affect the interpretation of the data sets.