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The normal distribution curve is one of the most important concepts in data analysis and statistics. It is a continuous probability distribution that is widely used to understand, summarize, and interpret numerical data. Many real-life variables, such as height, weight, examination scores, blood pressure, measurement errors, and some economic and social characteristics, may approximately follow a normal distribution. The normal distribution helps a data analyst understand how observations are distributed around the central value and how much variation exists in the data. A normal distribution is represented graphically by a smooth, bell-shaped curve. Therefore, it is commonly called the bell curve. The curve is symmetrical around its centre, which means that the left side is a mirror image of the right side. The highest point of the curve occurs at the centre, where the mean, median, and mode are equal. This property makes the normal distribution particularly useful for statistical analysis because the central tendency can be represented by a single point. The normal distribution is determined mainly by two parameters: the mean and the standard deviation. The mean, represented by μ, determines the location of the centre of the distribution. If the mean changes, the entire curve moves to the left or right. The standard deviation, represented by σ, measures the spread or dispersion of observations around ___ ___ ___ ______ ______ ____ ______ ________ ___ _____ ___.
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