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on the left and when it is skewed left it is on the right

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9y ago

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Where is the majority of data when the distribution is symetric?

Neither the left or the right but the middle


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.


When the majority of the data values fall to the right of the mean the distribution is said to be left skewed?

When the majority of the data values fall to the right of the mean, the distribution is indeed said to be left skewed, or negatively skewed. In this type of distribution, the tail on the left side is longer or fatter, indicating that there are a few lower values pulling the mean down. This results in the mean being less than the median, as the median is less affected by extreme values. Overall, left skewed distributions show that most data points are higher than the average.


What type of distribution pattern that occurs when the majority of the data values fall to the left of the mean?

positively skewed


What does a skewness of 1.27 mean?

A skewness of 1.27 indicates a distribution that is positively skewed, meaning that the tail on the right side of the distribution is longer or fatter than the left side. This suggests that the majority of the data points are concentrated on the left, with some extreme values on the right, pulling the mean higher than the median. In practical terms, this might indicate the presence of outliers or a few high values significantly affecting the overall distribution.


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.


What does it mean when the distribution of data is skewed to the right?

The distribution is unbalanced, because the right tail is larger than it would be if the distribution were balanced (symmetrical). Also called positive skew. See related link with diagrams that clarify this term.


How do you put the word distribution in a sentence?

1. The typical distribution of data in a bell curve shows that variations occur rarely and the majority of data is clustered around a mean or average. 2. The distribution of funds by the board of directors will be decided based on several factors that affect the organizations needs. 3. After the earthquake, the aid relief was quick to respond with distribution of water, food and medical supplies


Can frequency distribution contain qualitative data?

frequency distribution contain qualitative data


What is unimodal skewed?

Unimodal skewed refers to a distribution that has one prominent peak (or mode) and is asymmetrical, meaning it is not evenly balanced around the peak. In a right (or positively) skewed distribution, the tail on the right side is longer or fatter, indicating that most data points are concentrated on the left. Conversely, in a left (or negatively) skewed distribution, the tail on the left side is longer, with most data points clustered on the right. This skewness affects the mean, median, and mode of the data, typically pulling the mean in the direction of the tail.


What is a normal data set?

A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.


What is the mean of the sampling distribution of the sample mean?

Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.