An outlier is a number in a data set that is not around all the other numbers in the data. It will always affect the average; sometimes raising the average to a number higher than it should be, or lowering the average to something not reasonable.
Example:
Data Set - 2,2,3,5,6,1,4,9,31
Obviously 31 is the outlier. If you were to average these numbers it would be something greater than most of the numbers in your set due to the 31.
mean
Not necessarily.
You can always find measures of central tendency, such as the mean, median, and mode, in a histogram. A histogram visually represents the distribution of data and allows for easy identification of central tendencies. Additionally, these measures can also be found in other graphs like box plots and bar charts, but histograms are particularly effective for continuous data.
Its the one most commonly used but outliers can seriously distort the mean.
The mean and median are not always similar; their relationship depends on the distribution of the data. In a symmetrical distribution, such as a normal distribution, the mean and median are typically very close or identical. However, in skewed distributions, the mean can be significantly affected by outliers, causing it to differ from the median, which remains more representative of the central tendency. Thus, while they can be similar in certain cases, this is not universally true.
For which measure of central tendency will the sum of the deviations always be zero?
If the distribution is positively skewed distribution, the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency. This is true if we assume the distribution has a single mode.
Mean
If the distribution is positively skewed , then the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency (If it is a uni-modal distribution). If the distribution is negatively skewed then mean will always be the lowest estimate of central tendency and the mode will be the highest estimate of central tendency. In both positive and negative skewed distribution the median will always be between the mean and the mode. If a distribution is less symmetrical and more skewed, you are better of using the median over the mean.
mean
One of the characteristics of mean when measuring central tendency is that when there are positively skewed distributions, the mean is always greater than the median. Another characteristic is that when there are negatively skewed distributions, the mean is always less than the median.
Not necessarily.
You can always find measures of central tendency, such as the mean, median, and mode, in a histogram. A histogram visually represents the distribution of data and allows for easy identification of central tendencies. Additionally, these measures can also be found in other graphs like box plots and bar charts, but histograms are particularly effective for continuous data.
Its the one most commonly used but outliers can seriously distort the mean.
The mean and median are not always similar; their relationship depends on the distribution of the data. In a symmetrical distribution, such as a normal distribution, the mean and median are typically very close or identical. However, in skewed distributions, the mean can be significantly affected by outliers, causing it to differ from the median, which remains more representative of the central tendency. Thus, while they can be similar in certain cases, this is not universally true.
The tendency in nature for systems to become less ordered or organized is called entropy. This concept is central to the second law of thermodynamics, which states that in any natural process, the total entropy of an isolated system will always increase over time.
It depends on the boy it should be around 12-16 but there is always outlier's, when his voice starts to crack then his voice is starting to change.