Interpretation of a Right-Skewed Histogram
Interpreting a histogram of right-skewed data involves the knowledge of the data distribution as well as the impact of its skewness in favor of the right side. In a right-skewed histogram:
- Data points lie mainly on the left side of the graph; however, there is a long tail towards the right, stretching out.
- The fact that the histogram’s peak is found on the left means that smaller values occur more often compared to the remaining values in the data set.
- If the outliers (extreme values) are high to the distribution then they push the tail to the right of the curve.
- The mean value of a right-skewed distribution is higher than the median which is also higher than the mode.
- The lack of equality in the distribution of data is made obvious from the box plot whose line runs to the right. This may mean that there are a few high values which contribute greatly to the overall appearance of distribution.
- Data analysis is very dependent on being able to understand a right-skewed histogram since this can render the data distribution with outliers, and the total shape of the data.
Right Skewed Histogram
Right-skewed histogram is a graph showing the distribution of the data that is skewed to the right end, which means the tail of the graph is around the right side. Interpreting this type of histogram is crucial because it helps understand data distribution. In a right-skewed histogram, the bulk of the data points are settled on the left side, whereas a few extreme values drag the tail on the right. In the following article, we will learn the concept of histograms with a more narrow focus on right-skewed histograms for normal distributions.
Table of Content
- What is a Histogram?
- What is a Right-Skewed Histogram?
- How to Identify a Right-Skewed Histogram
- Interpretation of a Right-Skewed Histogram
- Mean, Median, and Mode in a Right Skewed Histogram
- How to Calculate Mean, Median, and Mode in a Right Skewed Histogram?
- Right Skewed Vs Left Skewed Histogram
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