Tag: statistics

Linear Correlation Coefficient Correlation adjusts covariance, so that the relationship between the two variables becomes easy and intuitive to interpret. Note: -1 <= correlation coefficient <= 1 Correlation coefficient of 1 implies, the entire variability of one variable is explained by the other. they are moving in same direction. Correlation coefficient of 0 implies, the…

Covariance If two variables are correlated (eg: house size and price), the main statistic used to measure this correlation between two variables is called covariance. Covariance gives a sense of direction in which the two variables are moving: one problem with covariance is the values can be of completely different scales for each dataset (eg:…

Standard Deviation In most analysis we perform, the standard deviation will be much more meaningful than variance. Standard deviation is the most common measure of variability for a single dataset. Standard deviation is the square root of variance. for both population and sample. Coefficient of variation (cv) Comparing Standard deviation between two datasets is meaningless.…

Variance Variance measures the dispersion of the set of data points around their mean. Example: Population: 1, 2, 3, 4, 5 The sample variance is higher than the population variance for the same data in the above example because: by calculating the sample variance of the population, we are telling that dataset 1,2,3,4&5 are sample…

Mean/Simple Average The mean is simple average of the dataset and it is denoted by Mean = (x1+x2+x3+….+xN)/N Mean is the most commonly used measure of central tendency the downside is it is easily affected by the outliers in the dataset Mean can be termed as Center of gravity of the given data. Median The…