# Asymmetrical Distribution.

A distribution is asymmetrical if it is not symmetrical. In other words, if you were to plot the data on a graph, the graph would not be symmetrical. There are two types of asymmetrical distributions: positively skew and negatively skew.

In a positively skew distribution, the data is spread out more on the right side of the graph. This means that the mean is usually greater than the median, and the median is usually greater than the mode.

In a negatively skew distribution, the data is spread out more on the left side of the graph. This means that the mean is usually less than the median, and the median is usually less than the mode.

##### What term is used to describe the measure of a symmetry of the distribution of real valued random variable?

There are a few different measures of symmetry for a distribution of real-valued random variables, but the most common one is called the skewness. Skewness is a measure of the asymmetry of the distribution; specifically, it is a measure of how far the distribution deviates from being symmetric. A distribution is symmetric if it is mirror-reflective; that is, if the left and right halves of the distribution are mirror images of each other. The skewness of a distribution is a measure of how far the distribution deviates from being symmetric.

### What is kurtosis in simple words?

Kurtosis measures the peakedness of a distribution. It is the measure of the "tail-ness" of a distribution. A distribution with a large kurtosis has a sharp peak and a short, fat tail. A distribution with a small kurtosis has a flat peak and a long, thin tail. How do you describe the distribution of data in statistics? There are many ways to describe the distribution of data in statistics. Some common ways are to describe the shape of the distribution, the center of the distribution, and the spread of the distribution.

The shape of the distribution can be described in terms of its symmetry and its tails. A distribution is symmetric if it looks the same on both sides of the center. A distribution is skewed if it is not symmetric. A distribution can also be described in terms of its tails. A distribution is heavy-tailed if the tails are long and thin. A distribution is light-tailed if the tails are short and fat.

The center of the distribution can be described in terms of the mean, median, and mode. The mean is the average of all the values in the distribution. The median is the value that is in the middle of the distribution. The mode is the value that occurs most often in the distribution.

The spread of the distribution can be described in terms of the range, variance, and standard deviation. The range is the difference between the highest and lowest values in the distribution. The variance is a measure of how spread out the values are. The standard deviation is a measure of how spread out the values are in relation to the mean. What is asymmetric data? Asymmetric data is data that is not symmetric. In other words, it is data that does not have a center point or line of symmetry.

##### What is kurtosis distribution?

Kurtosis is a measure of how heavily-tailed a distribution is. A distribution with a long tail is said to have high kurtosis, while a distribution with a short tail is said to have low kurtosis. Kurtosis is often used as a measure of how outlier-prone a distribution is; a distribution with high kurtosis is more likely to have extreme values (outliers) than a distribution with low kurtosis.

There are a few different ways to measure kurtosis, but the most common is the excess kurtosis, which is simply the kurtosis of a distribution minus the kurtosis of the normal distribution. The normal distribution has an excess kurtosis of zero, so a distribution with a positive excess kurtosis is said to be leptokurtic (heavy-tailed), while a distribution with a negative excess kurtosis is said to be platykurtic (light-tailed).

There are a few different ways to measure kurtosis, but the most common is the excess kurtosis, which is simply the kurtosis of a distribution minus the kurtosis of the normal distribution. The normal distribution has an excess kurtosis of zero, so a distribution with a positive excess kurtosis is said to be leptokurtic (heavy-tailed), while a distribution with a negative excess kurtosis is said to be platykurtic (light-tailed).

Kurtosis is a measure of how heavily-tailed a distribution is. A distribution with a long tail is said to have high kurtosis, while a distribution with a short tail is said to have low kurtosis. Kurtosis is often used as a measure of how outlier-prone a distribution is; a distribution with high kurtosis is more likely to have extreme values (outliers) than a distribution with low kurtosis.