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.
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.
Yes, every dataset with at least one value has a median characteristic, which represents the middle value when the data is ordered. If the dataset has an odd number of values, the median is the middle one, while if it has an even number of values, the median is the average of the two middle values. The median is a useful measure of central tendency, especially in skewed distributions, as it is less affected by extreme values compared to the mean.
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
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.
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.
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.
Because of water's nature to partially ionise itslef into hydronium and hydroxide ions, any solution of either a hydroxide or an acid will always be affected by this tendency, making the actual concentrations differ slightly from the expected ones.