Shapes of distributions can differ in skewness; these distributions are not symmetrical distributions. Instead, they have more points plotted on one side of the mean than on the other.

The shape of a distribution includes the following three aspects: Overall shape: what the distribution looks like, e.g., bell-shaped, J-shaped, triangular, and uniform.

The shape of a distribution is described by its number of peaks and by its possession of symmetry, its tendency to skew, or its uniformity. (Distributions that are skewed have more points plotted on one …

A distribution that is not symmetric must have values that tend to be more spread out on one side than on the other. In this case, we say that the distribution is skewed.

Mar 1, 2022 · The shape of distribution helps us understand the spread and behavior of a given distribution. With visual representations such as the distribution’s shapes, we can easily represent …

When we describe shapes of distributions, we commonly use words like symmetric, left-skewed, right-skewed, bimodal, and uniform. Not every distribution fits one of these descriptions, but they are still a …

Jan 9, 2026 · Recognizing the shape helps us choose the right summary statistics, spot unusual values, and prepare for more advanced ideas like probability and inference. In this section, we’ll explore the …

This chapter examines how data distribution patterns reveal underlying business processes and inform analytical decision-making. Key concepts include normal distribution characteristics, skewed pattern …

We saw in the section on distributions in Chapter 1 that shapes of distributions can differ in skew and/or kurtosis. This section presents numerical indexes of these two measures of shape.

While distribution displays the values the variable takes and how often, shape describes the data points as a whole. This tutorial will use qualifying descriptors to identify how the distribution of a data set …