No, they are rarely the same.
nop its not
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
Yes, that's true. In a normal distribution, a smaller standard deviation indicates that the data points are closer to the mean, resulting in a taller and narrower curve. Conversely, a larger standard deviation leads to a wider and shorter curve, reflecting more variability in the data. Thus, the standard deviation directly affects the shape of the normal distribution graph.
Yes, the normal distribution is uniquely defined by its mean and standard deviation. The mean determines the center of the distribution, while the standard deviation indicates the spread or dispersion of the data. Together, these two parameters specify the shape and location of the normal distribution curve.
Because the standard deviation is one of the two parameters (the other being the mean) which define the Normal curve. The mean defines the location and the standard deviation defines its shape.
Mean = 0 Standard Deviation = 1
1
nop its not
The distance between the middle and the inflection point is the standard deviation.
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
It is 15 points.
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
Yes, that's true. In a normal distribution, a smaller standard deviation indicates that the data points are closer to the mean, resulting in a taller and narrower curve. Conversely, a larger standard deviation leads to a wider and shorter curve, reflecting more variability in the data. Thus, the standard deviation directly affects the shape of the normal distribution graph.
Yes, the normal distribution is uniquely defined by its mean and standard deviation. The mean determines the center of the distribution, while the standard deviation indicates the spread or dispersion of the data. Together, these two parameters specify the shape and location of the normal distribution curve.
Because the standard deviation is one of the two parameters (the other being the mean) which define the Normal curve. The mean defines the location and the standard deviation defines its shape.
The mean of a standard normal curve is 0. This curve, which is a type of probability distribution known as the standard normal distribution, is symmetric and bell-shaped, centered around the mean. Additionally, the standard deviation of a standard normal curve is 1, which helps define the spread of the data around the mean.
The width of the peak of a normal curve depends primarily on the standard deviation of the distribution. A larger standard deviation results in a wider and flatter curve, indicating greater variability in the data, while a smaller standard deviation yields a narrower and taller peak, indicating less variability. Thus, the standard deviation is crucial for determining the spread of the data around the mean.