The median is Q2, if it is on the right side of the box, then then it is close to Q3 than it is to Q1. If the right line ( whisker) is longer than the left, it mean the biggest outlier is farther from Q3 than the smallest outlier is from Q1. All of this means the population from which the data was sampled was skewed to the right.
In the majority of Empirical cases the mean will not be equal to the median, so the event is hardly unusual. If the mean is greater, then the distribution is poitivelt skewed (skewed to the right).
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.
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.
The question is how do the mean and median affect the distribution shape. In a normal curve, the mean and median are both in the same point. ( as is the mode) If a distribution is skewed, its tail is either on the right or the left. If a distribution is skewed the median may be a better value to use than the mean since it has less effect on the shape. Also is there are large outliers, the median has less effect and is better to use. So the mean has a bigger effect on the shape many times than the median.
No, as you said it is right skewed.
When a distribution is skewed to the right, the mean is greater than median.
In the majority of Empirical cases the mean will not be equal to the median, so the event is hardly unusual. If the mean is greater, then the distribution is poitivelt skewed (skewed to the right).
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)
Positively skewed.
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.
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.
A skewed distribution typically has one tail that is longer or fatter than the other. In a right-skewed distribution, the tail on the right side is longer, while in a left-skewed distribution, the left tail is longer. Therefore, a skewed distribution has one dominant tail, but it can be characterized by its direction (right or left).
A skewed box plot is characterized by the asymmetrical distribution of data, indicated by the position of the median line within the box and the lengths of the whiskers. In a right-skewed box plot, the median is closer to the lower quartile, with a longer upper whisker, while in a left-skewed box plot, the median is nearer to the upper quartile, accompanied by a longer lower whisker. Additionally, the presence of outliers may further emphasize the skewness of the data. Overall, the visual representation helps to quickly assess the distribution and identify potential outliers.
the shape of the curve skewed is "right"
In mathematics, "skewed" refers to the asymmetry in the distribution of data. A skewed distribution can be either positively skewed, where the tail on the right side is longer or fatter, or negatively skewed, where the tail on the left side is longer or fatter. This indicates that the mean and median of the data may not align, often with the mean being pulled in the direction of the skew. Understanding skewness helps in analyzing the characteristics of the data and choosing appropriate statistical methods.
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.
Unimodal skewed refers to a distribution that has one prominent peak (or mode) and is asymmetrical, meaning it is not evenly balanced around the peak. In a right (or positively) skewed distribution, the tail on the right side is longer or fatter, indicating that most data points are concentrated on the left. Conversely, in a left (or negatively) skewed distribution, the tail on the left side is longer, with most data points clustered on the right. This skewness affects the mean, median, and mode of the data, typically pulling the mean in the direction of the tail.