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Altering the mean causes the curve to shift along the number line, but changing the standard deviation causes the curve to stretch or squash. We may vary the form and position of the distribution by adjusting the mean and standard deviation. Another defining characteristic of the normal distribution is the empirical rule, we’ll be elaborating on this next.
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The area under the curve is 1, its mean, median, and mode are equal, it’s also symmetrical around the mean. The normal distribution has various properties that make it extremely useful. Its probability density curve is shown below(fig.1): Characteristics of Normal DistributionĪ normal distribution may be described using only two parameters, the mean and standard deviation, which are denoted by the Greek letters mu (?) and sigma (?). Talking about probability distributions, Normal distribution is the most popular one, and hence the one people often tend to begin with, so let's dig into this without any further ado. It is also known as the Gaussian distribution after Carl Friedrich Gaus, who discovered it. It not only approximates a wide range of variables, but actions based on its insights have a strong track record. When it comes to statistics, the normal distribution is essential. It may also be found in data science and machine learning, as well as several unexpected real-world events. The normal distribution is a fundamental notion in statistics. We'll also be discussing a way to visualize the distribution with the help of a Histogram. We'll be covering one such distribution in this article: The Normal Distribution. Many fascinating insights may be derived from our data by understanding the distribution of the data. Visualizing your data may also yield unexpected outcomes. Rather than studying them in tabular form, visualizing them enables a more natural and rapid comprehension. When working with data, visualization is the greatest approach to immediately grasp it.