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Q: In your own words describe the standard normal distribution?

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A normal distribution of data is one in which the majority of data points are relatively similar, meaning they occur within a small range of values with fewer outliers on the high and low ends of the data range. When data are normally distributed, plotting them on a graph results a bell-shaped and symmetrical image often called the bell curve. In such a distribution of data, mean, median, and mode are all the same value and coincide with the peak of the curve. However, in social science, a normal distribution is more of a theoretical ideal than a common reality. The concept and application of it as a lens through which to examine data is through a useful tool for identifying and visualizing norms and trends within a data set. Properties of the Normal Distribution One of the most noticeable characteristics of a normal distribution is its shape and perfect symmetry. If you fold a picture of a normal distribution exactly in the middle, you'll come up with two equal halves, each a mirror image of the other. This also means that half of the observations in the data falls on either side of the middle of the distribution. The midpoint of a normal distribution is the point that has the maximum frequency, meaning the number or response category with the most observations for that variable. The midpoint of the normal distribution is also the point at which three measures fall: the mean, median, and mode. In a perfectly normal distribution, these three measures are all the same number. In all normal or nearly normal distributions, there is a constant proportion of the area under the curve lying between the mean and any given distance from the mean when measured in standard deviation units. For instance, in all normal curves, 99.73 percent of all cases fall within three standard deviations from the mean, 95.45 percent of all cases fall within two standard deviations from the mean, and 68.27 percent of cases fall within one standard deviation from the mean. Normal distributions are often represented in standard scores or Z scores, which are numbers that tell us the distance between an actual score and the mean in terms of standard deviations. The standard normal distribution has a mean of 0.0 and a standard deviation of 1.0. Examples and Use in Social Science Even though a normal distribution is theoretical, there are several variables researchers study that closely resemble a normal curve. For example, standardized test scores such as the SAT, ACT, and GRE typically resemble a normal distribution. Height, athletic ability, and numerous social and political attitudes of a given population also typically resemble a bell curve. The ideal of a normal distribution is also useful as a point of comparison when data are not normally distributed. For example, most people assume that the distribution of household income in the U.S. would be a normal distribution and resemble the bell curve when plotted on a graph. This would mean that most U.S. citizens earn in the mid-range of income, or in other words, that there is a healthy middle class. Meanwhile, the numbers of those in the lower economic classes would be small, as would the numbers in the upper classes. However, the real distribution of household income in the U.S. does not resemble a bell curve at all. The majority of households fall into the low to the lower-middle range, meaning there are more poor people struggling to survive than there are folks living comfortable middle-class lives. In this case, the ideal of a normal distribution is useful for illustrating income inequality.

There is not enough information to give an answer. You need to know something about the distribution of the variable in the population. It is reasonable to assume that it is Gaussian (Normal)? Secondly, what power do you require of the test statistic? In other words, what level of significance do you require and that, in turn, depends on the "cost" of getting it wrong!

Exactly as in the question: standard deviation!

You can describe it using words or in graph form.

equal and sided

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Ampere (A) is the standard, base unit.

It is the so-called "half-normal distribution." Specifically, let X be a standard normal variate with cumulative distribution function F(z). Then its cumulative distribution function G(z) is given by Prob(|X| < z) = Prob(-z < X < z) = Prob(X < z) - Prob(X < -z) = F(z) - F(-z). Its probability distribution function g(z), z >= 0, therefore equals g(z) = Derivative of (F(z) - F(-z)) = f(z) + f(-z) {by the Chain Rule} = 2f(z) because of the symmetry of f with respect to zero. In other words, the probability distribution function is zero for negative values (they cannot be absolute values of anything) and otherwise is exactly twice the distribution of the standard normal.

Nasty, nice, normal, Nitwit

A normal distribution of data is one in which the majority of data points are relatively similar, meaning they occur within a small range of values with fewer outliers on the high and low ends of the data range. When data are normally distributed, plotting them on a graph results a bell-shaped and symmetrical image often called the bell curve. In such a distribution of data, mean, median, and mode are all the same value and coincide with the peak of the curve. However, in social science, a normal distribution is more of a theoretical ideal than a common reality. The concept and application of it as a lens through which to examine data is through a useful tool for identifying and visualizing norms and trends within a data set. Properties of the Normal Distribution One of the most noticeable characteristics of a normal distribution is its shape and perfect symmetry. If you fold a picture of a normal distribution exactly in the middle, you'll come up with two equal halves, each a mirror image of the other. This also means that half of the observations in the data falls on either side of the middle of the distribution. The midpoint of a normal distribution is the point that has the maximum frequency, meaning the number or response category with the most observations for that variable. The midpoint of the normal distribution is also the point at which three measures fall: the mean, median, and mode. In a perfectly normal distribution, these three measures are all the same number. In all normal or nearly normal distributions, there is a constant proportion of the area under the curve lying between the mean and any given distance from the mean when measured in standard deviation units. For instance, in all normal curves, 99.73 percent of all cases fall within three standard deviations from the mean, 95.45 percent of all cases fall within two standard deviations from the mean, and 68.27 percent of cases fall within one standard deviation from the mean. Normal distributions are often represented in standard scores or Z scores, which are numbers that tell us the distance between an actual score and the mean in terms of standard deviations. The standard normal distribution has a mean of 0.0 and a standard deviation of 1.0. Examples and Use in Social Science Even though a normal distribution is theoretical, there are several variables researchers study that closely resemble a normal curve. For example, standardized test scores such as the SAT, ACT, and GRE typically resemble a normal distribution. Height, athletic ability, and numerous social and political attitudes of a given population also typically resemble a bell curve. The ideal of a normal distribution is also useful as a point of comparison when data are not normally distributed. For example, most people assume that the distribution of household income in the U.S. would be a normal distribution and resemble the bell curve when plotted on a graph. This would mean that most U.S. citizens earn in the mid-range of income, or in other words, that there is a healthy middle class. Meanwhile, the numbers of those in the lower economic classes would be small, as would the numbers in the upper classes. However, the real distribution of household income in the U.S. does not resemble a bell curve at all. The majority of households fall into the low to the lower-middle range, meaning there are more poor people struggling to survive than there are folks living comfortable middle-class lives. In this case, the ideal of a normal distribution is useful for illustrating income inequality.

You can used the words: "Accomplished in" amd state them.

There are numerous words to describe someone who sets a standard, with role-model and idol being the major two.

The Normal curve is a graph of the probability density function of the standard normal distribution and, as is the case with any continuous random variable (RV), the probability that the RV takes a value in a given range is given by the integral of the function between the two limits. In other words, it is the area under the curve between those two values.

Some other words used to describe tepid are words like; warm, luke warm, slightly heated, indifferent, unenthusiastic, milk-warm, normal temperature, lifeless, spiritless, moderate and unlively.

big words to describe excelence

Hydrogen peroxide is the standard english name. It doesn't have a common non-chemical name.

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