The shape of a t distribution changes with degrees of freedom (df). As the the df gets very large the shape of the t distribution will begin to look similar to that of a normal distribution. However, the t distribution has more variability than a normal distribution; especially when the df are small. When this is the case the t distribution will be flatter and more spread out than the normal distributions.
It is a measure of the spread of the distribution: whether all the observations are clustered around a central measure or if they are spread out.
Population distribution refers to the patterns that a population creates as they spread within an area. A sampling distribution is a representative, random sample of that population.
Outliers will make give the graph a long tail (or tails). Overall, the graph will be flatter and wider.
It is a measure of the spread of the distribution. The greater the standard deviation the more variety there is in the observations.
An uneven distribution means that an area which is uneven to the area beside the area which is uneven
The standard deviation (SD) is a measure of spread so small sd = small spread. So the above is true for any distribution, not just the Normal.
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.
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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.
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.
The two distributions are symmetrical about the same point (the mean). The distribution where the sd is larger will be more flattened - with a lower peak and more spread out.
It is a measure of the spread of the distribution: whether all the observations are clustered around a central measure or if they are spread out.
With a 10 point grading scale the results (of a test etc.) are given a value between 0 and 9 or 1 and 10. If the grading is "on a curve" than the distribution of the various grades is spread on a Gaussian normal distribution.
Yes. By definition. A normal distribution has a bell-shaped density curve described by its mean and standard deviation. The density curve is symmetrical(i.e., an exact reflection of form on opposite sides of a dividing line), and centered about (divided by) its mean, with its spread (width) determined by its standard deviation. Additionally, the mean, median, and mode of the distribution are equal and located at the peak (i.e., height of the curve).
In a statistical sense, spread, otherwise known as statistical dispersion, is one of various measures of distribution.
Population distribution refers to the patterns that a population creates as they spread within an area. A sampling distribution is a representative, random sample of that population.