The concept of autocorrelation is most often discussed in the context of time series data in which observations occur at different points in time (e.g., air temperature measured on different days of the month). For example, one might expect the air temperature on the 1st day of the month to be more similar to the temperature on the 2nd day compared to the 31st day. If the temperature values that occurred closer together in time are, in fact, more similar than the temperature values that occurred farther apart in time, the data would be autocorrelated. Imagine seasonal data that rises and falls. Go Deeper: Autocorrelation refers to the degree of correlation between the values of the same variables across different observations in the data.
Animated Lean Six Sigma Video Tutorials.Statistical Analysis - Hypothesis Testing.
