No, the mean cannot be greater than the greatest value in a data set. The mean is calculated by summing all the values and dividing by the number of values, which means it will always fall within the range of the data set. Therefore, the mean will always be less than or equal to the maximum value.
When the data set consistys of a single value.
The least value of the data set is called the minimum.
An outlier does affect the mean of the data. How it's affected depends on how many data points there are, how far from the data the outlier is, whether it is greater than the mean (increases mean) or less than the mean (decreases the mean).
Yes.
The data point is close to the expected value.
No.
It means that you take the absolute values of different numbers, and then compare them to see which one is greater.
In a normal distribution the mean, median and mode are all the same value.
If you mean: 8.080 8.088 and 0.888 then it is 8.088 that has the greatest value
When the data set consistys of a single value.
To become of greater value or be worth more.
To become of greater value or be worth more.
The mean of a set of numbers is their distinct average. The median is the absolute middle value. If the mean is higher it means that the overall set is heavily weighted at the upper end.
The least value of the data set is called the minimum.
An outlier does affect the mean of the data. How it's affected depends on how many data points there are, how far from the data the outlier is, whether it is greater than the mean (increases mean) or less than the mean (decreases the mean).
Subtracting the data value from the mean yields the deviation of that data point from the mean. This value indicates how far and in what direction the data point lies from the average, with positive values representing data points above the mean and negative values indicating those below it. This calculation is essential for understanding variability and dispersion in a dataset.
The approximate average of the squares of the distance each value is from the mean is known as the variance. It is calculated by taking the differences between each value and the mean, squaring those differences, and then averaging them. Variance provides a measure of how spread out the values are around the mean. A higher variance indicates greater dispersion in the data set.