If the mean, mode, and median are different, the distribution is likely skewed. For instance, if the mean is greater than the median, the distribution may be positively skewed (right-skewed), indicating a tail extending towards higher values. Conversely, if the mean is less than the median, the distribution may be negatively skewed (left-skewed), with a tail extending towards lower values. In both cases, the presence of outliers or a non-symmetrical spread can contribute to these differences.
To determine if the data in a line plot is skewed left, right, or not skewed, you would need to observe the distribution of the data points. If the tail on the left side is longer or fatter, it is left-skewed; if the tail on the right side is longer or fatter, it is right-skewed. If the data points are evenly distributed around a central value, it is not skewed. Without seeing the actual plot, I can't provide a definitive answer.
The retaining wall is skewed perfectly.
The term used to describe this shape of a distribution is "negatively skewed" or "left-skewed." In a negatively skewed distribution, most of the data points are concentrated on the higher end, with a tail extending towards the lower end. This results in a longer left tail and a peak that is shifted to the right.
Nobody invented skewed distributions! There are more distributions that are skewed than are symmetrical, and they were discovered as various distribution functions were discovered.
A distribution or set of observations is said to be skewed left or negatively skewed if it has a longer "tail" of numbers on the left. The mass of the distribution is more towards the right of the figure rather than the middle.
A distribution or set of observations is said to be skewed right or positively skewed if it has a longer "tail" of numbers on the right. The mass of the distribution is more towards the left of the figure rather than the middle.
If the mean, mode, and median are different, the distribution is likely skewed. For instance, if the mean is greater than the median, the distribution may be positively skewed (right-skewed), indicating a tail extending towards higher values. Conversely, if the mean is less than the median, the distribution may be negatively skewed (left-skewed), with a tail extending towards lower values. In both cases, the presence of outliers or a non-symmetrical spread can contribute to these differences.
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
To determine if the data in a line plot is skewed left, right, or not skewed, you would need to observe the distribution of the data points. If the tail on the left side is longer or fatter, it is left-skewed; if the tail on the right side is longer or fatter, it is right-skewed. If the data points are evenly distributed around a central value, it is not skewed. Without seeing the actual plot, I can't provide a definitive answer.
The retaining wall is skewed perfectly.
As the mean is greater than the median it will be positively skewed (skewed to the right), and if the median is larger than the mean it will be negatively skewed (skewed to the left)
Due to systematic error, my results are skewed.
The term used to describe this shape of a distribution is "negatively skewed" or "left-skewed." In a negatively skewed distribution, most of the data points are concentrated on the higher end, with a tail extending towards the lower end. This results in a longer left tail and a peak that is shifted to the right.
When a set of votes has been skewed it means that either the mean is higher than the median or lower. If it is higher the vote is said to be skewed to the right and when lower it is skewed to the left.
Symmetric
Nobody invented skewed distributions! There are more distributions that are skewed than are symmetrical, and they were discovered as various distribution functions were discovered.