## The relationship between variables Β§

If the data is close to the trend line, we can say the relationship is strong. If itβs the opposite, we can say the relationship is weak. These are educated guesses based on the trend.

## Correlation value Β§

Weak relationship has a small correlation value, while strong relationship has a large correlation value.

The max absolute correlation value = 1, when every data point is on the line. It can happen when the slope is steep or mild.

Note: because any two dots can draw a straight line, we should not have much confidence to a correlation value = 1 when there are only two points.

### Positive, negative and zero correlation value Β§

Similar to covariance value, a negative correlation value suggests a negative trend.

The worse the fit, the closer to 0. When thereβs no relationship, it will be a straight line and correlation = 0.

## The connection between p-value and correlation Β§

For correlation, a P-value tells us the probability that randomly drawn dots will result in a similar strong relationship. The smaller it is, the more confidence we have in the predictions (because itβs less likely to have the coincidence that the data randomly fits the line).

### Educated guesses from correlation Β§

Even we have lots of data points and a small p-value (more confidence), if the correlation value is small, the guesses would be less accurate.

The estimates:

## Correlation vs R-squared Β§

Correlation is still not easy to interpret, and R squared can help us understand.