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.
A normal distribution is not skewed. Skewness is a measure of how the distribution has been pulled away from the normal.A feature of a distribution is the extent to which it is symmetric.A perfectly normal curve is symmetric - both sides of the distribution would exactly correspond if the figure was folded across its median point.It is said to be skewed if the distribution is lop-sided.The word, skew, comes from derivations associated with avoiding, running away, turning away from the norm.So skewed to the right, or positively skewed, can be thought of as grabbing the positive end of the bell curve and dragging it to the right, or positive, direction to give it a long tail in the positive direction, with most of the data still concentrated on the left.Then skewed to the left, or negatively skewed, can be thought of as grabbing the negative end of the bell curve and dragging it to the left, or negative, direction to give it a long tail in the negative direction, with most of the data still bunched together on the right.Warning: A number of textbooks are not correct in their use of the term 'skew' in relation to skewed distributions, especially when describing 'skewed to the right' or 'skewed to the left'.
Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.The Shape of a HistogramA histogram is unimodal if there is one hump, bimodal if there are two humps and multimodal if there are many humps. A nonsymmetric histogram is called skewed if it is not symmetric. If the upper tail is longer than the lower tail then it is positively skewed. If the upper tail is shorter than it is negatively skewed.Unimodal, Symmetric, NonskewedNonsymmetric, Skewed RightBimodal
There isn't a specific chart for skewed data, but you could use a number of different charts to show that data is skewed. An Area chart could be used for example, or a column chart could also work. It would depend in the nature of the data.
mode
i) Since Mean<Median the distribution is negatively skewed ii) Since Mean>Median the distribution is positively skewed iii) Median>Mode the distribution is positively skewed iv) Median<Mode the distribution is negatively skewed
A positively skewed or right skewed distribution means that the mean of the data falls to the right of the median. Picturewise, most of the frequency would occur to the left of the graph.
When the data distribution is negatively skewed.
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.
A normal distribution is not skewed. Skewness is a measure of how the distribution has been pulled away from the normal.A feature of a distribution is the extent to which it is symmetric.A perfectly normal curve is symmetric - both sides of the distribution would exactly correspond if the figure was folded across its median point.It is said to be skewed if the distribution is lop-sided.The word, skew, comes from derivations associated with avoiding, running away, turning away from the norm.So skewed to the right, or positively skewed, can be thought of as grabbing the positive end of the bell curve and dragging it to the right, or positive, direction to give it a long tail in the positive direction, with most of the data still concentrated on the left.Then skewed to the left, or negatively skewed, can be thought of as grabbing the negative end of the bell curve and dragging it to the left, or negative, direction to give it a long tail in the negative direction, with most of the data still bunched together on the right.Warning: A number of textbooks are not correct in their use of the term 'skew' in relation to skewed distributions, especially when describing 'skewed to the right' or 'skewed to the left'.
positively skewed
It is a positively skewed distribution.
When the data are skewed to the right the measure of skewness will be positive.
Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.The Shape of a HistogramA histogram is unimodal if there is one hump, bimodal if there are two humps and multimodal if there are many humps. A nonsymmetric histogram is called skewed if it is not symmetric. If the upper tail is longer than the lower tail then it is positively skewed. If the upper tail is shorter than it is negatively skewed.Unimodal, Symmetric, NonskewedNonsymmetric, Skewed RightBimodal
The population data may be skewed and thus the mean is not a valid statistic. If mean > median, the data will be skewed to the right. If median > mean, the data is skewed to the left.
It is a descriptive statistical measure used to measure the shape of the curve drawn from the frequency distribution or to measure the direction of variation. It is a measure of how far positively skewed (below the mean) or negatively skewed (above the mean) the majority (that's where the mode comes in) of the data lies. Useful when conducting a study using histograms. (mean - mode) / standard deviation. or [3(Mean-Median)]/Standard deviation
There isn't a specific chart for skewed data, but you could use a number of different charts to show that data is skewed. An Area chart could be used for example, or a column chart could also work. It would depend in the nature of the data.