The normal distribution allows you to measure the distribution of a set of data points. It helps to determine the average (mean) of the data and how spread out the data is (standard deviation). By using the normal distribution, you can make predictions about the likelihood of certain values occurring within the data set.
In science, "normal" typically means something that is within expected parameters or conforms to a standard. For example, a "normal distribution" refers to a bell-shaped curve that represents the expected distribution of a set of data points.
The answer will depend on the distribution of the weights. There is no basis for assuming that the distribution is normal, or even symmetrical.
Factors that influence the global distribution of ecosystems include climate, topography, soil quality, and human activities such as deforestation and urbanization. Climate, in particular, plays a key role in determining the type of vegetation that can thrive in a certain region, while topography and soil quality affect the overall biodiversity of an ecosystem. Human activities can disrupt natural ecosystems and lead to changes in distribution patterns.
The bell curve, also known as the normal distribution, is a symmetrical probability distribution that follows the empirical rule. The empirical rule states that for approximately 68% of the data, it lies within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations when data follows a normal distribution. This relationship allows us to make predictions about data distribution based on these rules.
le standard normal distribution is a normal distribution who has mean 0 and variance 1
The mean of a standard normal distribution is 0.
The standard normal distribution is a normal distribution with mean 0 and variance 1.
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
A normal distribution can have any value for its mean and any positive value for its variance. A standard normal distribution has mean 0 and variance 1.
In a normal distribution half (50%) of the distribution falls below (to the left of) the mean.
The standard normal distribution has a mean of 0 and a standard deviation of 1.
The normal distribution can have any real number as mean and any positive number as variance. The mean of the standard normal distribution is 0 and its variance is 1.
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.
The distribution of the sample mean is bell-shaped or is a normal distribution.