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Does a bimodal distribution have to be symmetrical?
Distributions don’t have to be unimodal to be symmetric. They can be bimodal (two peaks) or multimodal (many peaks). The following bimodal distribution is symmetric, as the two halves are mirror images of each other.
What is the shape of a bimodal distribution?
Bimodal: A bimodal shape, shown below, has two peaks. This shape may show that the data has come from two different systems. If this shape occurs, the two sources should be separated and analyzed separately. Skewed right: Some histograms will show a skewed distribution to the right, as shown below.
Can a distribution have 2 modes?
In statistics, a bimodal distribution is a probability distribution with two different modes, which may also be referred to as a bimodal distribution.
What do you mean by bimodal distribution in statistics?
A bimodal distribution is a probability distribution with two modes. We often use the term “mode” in descriptive statistics to refer to the most commonly occurring value in a dataset, but in this case the term “mode” refers to a local maximum in a chart.
How to interpret a symmetric bimodal histogram?
Recommended Next Step If the histogram indicates a symmetric, bimodal distribution, the recommended next steps are to: Do a run sequence plotor a scatter plotto check for sinusoidality. Do a lag plotto check for sinusoidality. If the lag plot is elliptical, then the data are sinusoidal.
Which is the best example of a unimodal distribution?
The normal distribution is the classic example of a unimodal distribution. The histogram shown above illustrates data from a bimodal (2 peak) distribution. The histogram serves as a tool for diagnosing problems such as bimodality.
How to determine the best fit symmetric distribution?
If the data are sinusoidal, then a spectral plotis used to graphically estimate the underlying sinusoidal frequency. If the data are not sinusoidal, then a Tukey Lambda PPCC plotmay determine the best-fit symmetric distribution for the data. The data may be fit with a mixture of two distributions.