Here are the essential concepts you must grasp in order to answer the question correctly.
Normal Distribution
The normal distribution is a continuous probability distribution characterized by its bell-shaped curve, symmetric around the mean. It is defined by two parameters: the mean (average) and the standard deviation (spread). In this case, a mean of 450 indicates the center of the distribution, while a standard deviation of 50 indicates how spread out the values are around the mean.
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Mean and Standard Deviation
The mean is the average value of a dataset, serving as the central point of the normal curve. The standard deviation measures the dispersion of data points from the mean; a larger standard deviation results in a wider curve. In this scenario, the mean of 450 and a standard deviation of 50 suggest that most data points will fall within the range of 350 to 550, encompassing approximately 68% of the data within one standard deviation.
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Features of the Normal Curve
The normal curve has several key features: it is symmetric about the mean, with the highest point at the mean, and it approaches the horizontal axis but never touches it. The area under the curve represents the total probability, which equals 1. Additionally, the empirical rule states that about 68% of data falls within one standard deviation, 95% within two, and 99.7% within three standard deviations from the mean.
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