We posted the Linear Regression so we can explain the use on our Relativistic Market data.

The two parts we used are the: Slope and R-Squared Correlation.

These help in understanding the direction and chaos in the data.

The Slope is a measure of how slanted the data is on average.

If the Slope is +, then the overall data is sloping up from left to right, like this:  /

If the Slope is -, then the overall data is sloping down from left to right, like this:  \

Slope values close to 0 are an indication the overall data is flat, like this:  ^_

Keep in mind, it's on average, because the next measure, R-Squared Correlation, is actually a chaos reading; with a little massage of the value.

R-Squared Correlation tells us how close the data is to the regression line.

The closer the data is to the line, the closer to 1 the R-Squared value is.

Values closer to 0 means chaos.

Since we need a good measure of chaos that is positive and negative, we find a chaos measure with this expression: 1 - 2R²

Now, you'd think chaos would be a bad thing, but that's not the case here.

All this does is tells us how closely the data follows the line.

To get the goodness and badness of the data, you have to use both of these values together and place them in to proper context before making that determination.

In the Relative Market data case, positive slope and positive chaos is market growth.

With negative slope and positive chaos, it could be an indication of market decline.

Relativistic Market data that has a slope near zero and any positive/negative chaos is an indication the market is not going anywhere fast.

We'll see how this works in another post and see why we are concerned about what is happening in the market.