After calculating the mean and standard deviation, how do we interpret the result, and when a variable look strange?
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
After calculating the
The mean is a measure of central tendency and the standard deviation is a measure of dispersion in the data.
The measure of central tendency gives the value around which most of the observations lie. It simply represents, the most expected value in the data. It is also called a measure of location because it shows the center or the centralized value around which maximum observations in the data fall.
By calculating the mean we get an idea that the center of the distribution around which all other observations fall is the mean value. It is the most suitable representative of other observations in the data.
Dispersion is the variability of data, i.e., by how much, the observations vary from each other. Especially, how much the observations deviate from the centralized value or mean.
Standard deviation is a measure of dispersion that shows the average standard deviations of observations from their mean.
By calculating mean and standard deviation we get a summary of the data that tells us about the most expected location (mean) around which all other observations cluster and an idea of average deviations of observations from their mean (standard deviation).
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