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Q: How many tails in skewed distribution?
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If a great many data values cluster to the left of a data distribution which then tails off to the right the distribution is referred to as?

It is a positively skewed distribution.


What is a negative frequency distribution?

If most the population has many high scores, the distribution is negatively skewed. If most have many low scores, it is positively skewed


When is data negatively or positively skewed?

i) Since Mean<Median the distribution is negatively skewed ii) Since Mean>Median the distribution is positively skewed iii) Median>Mode the distribution is positively skewed iv) Median<Mode the distribution is negatively skewed


What does a skew look like?

A distribution is skewed if one of its tails is longer than the other. The first distribution shown has a positive skew. This means that it has a long tail in the positive direction.


When a population distribution is right skewed is the sampling distribution normal?

No, as you said it is right skewed.


Who invented skewed distribution?

Nobody invented skewed distributions! There are more distributions that are skewed than are symmetrical, and they were discovered as various distribution functions were discovered.


Is a normal distribution a skewed?

No.


Can a normal distribution curve be symmetric or left-skewed or right-skewed?

Symmetric


What is a positively skewed distribution?

A positively skewed or right skewed distribution means that the mean of the data falls to the right of the median. Picturewise, most of the frequency would occur to the left of the graph.


When is the median the most appropriate center for a distribution?

The median is the most appropriate center when the distribution is very skewed or if there are many outliers.


What if the mean is greater than the median?

In the majority of Empirical cases the mean will not be equal to the median, so the event is hardly unusual. If the mean is greater, then the distribution is poitivelt skewed (skewed to the right).


How many tails in normal distribution?

2