When we talk about **univariate analysis**, we will normally find elements such as:

**Histograms**.**Percentiles**: The**percentile**(or**centile**) is the value of the variable below which a certain percentage of observations fall; for example, the 20th percentile is the value (or score) below which 20 per cent of the observations fall.**Statistical moments**: Mean, median, standard deviation.**The moment above zero**: Mean, median, second moment.**Moments above the mean**: Variance, standard deviation, skewness, kurtosis.

In the **statistical and graphical analysis**, specifically in the **univariate analysis**, we can observe two main elements to take into account:

**Kurtosis**: The fourth central moment is whether the distribution is tall and thin or short and square, compared to the**normal distribution**of the same**variance**.**Skewness**: The**measure of the skewness of the probability distribution**of a real-valued random variable. The value of skewness can be positive or negative or even undefined. Qualitatively, a negative skewness value indicates that the tail of the left-hand side of the probability density function is longer than that of the right-hand side. Most of the values (possibly including the**median**) lie to the right of the mean.

## Kurtosis

From the highest peak to the lowest peak:

Kurtosis: Leptokurtic, Mesokurtic and Platykurtic:

**Leptokurtic**: “Lepto” (meaning thin). In terms of shape, a leptokurtic distribution has a sharper peak around the mean.**Mesocurtic**: Semicircular distribution. Elevation in the cosine distribution. Uniform distribution.**Platicurtic**: “Plati” comes from tabla. It has a platicurtic distribution with a lower and broader peak around the mean in terms of shape.

## Skewness

Types of asymmetry: negative and positive skewness

**Negative skewness**: The height on the left is longer; the mass of the distribution is concentrated on the figure’s right. The distribution is said to be left (observations): 1,1001,1002,1003.**Positive skewness**: The high on the right is longer; the mass of the distribution is concentrated on the figure’s left. It has relatively few high values. The distribution is said to be right (observation) 1,2,3,1000.