Part A
The group of points labeled "T" can be thought of as the main cluster of points. Your teacher might have given another name for it, but this is how I think about it. All of these points are very close to a straight line.
The point R is known as an outlier. It is fairly far from the main cluster, in that it's not near the regression line. You can think of the points in the main cluster as houses along/near a straight road. The outlier house is unfortunately far from that road.
One possible reason for point R is that the student is very talented and can pick up music really easy, thereby not needing that much practice. Another reason is that the student already had tons of prior practice and experience, so they don't need as much practice currently.
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Part B
We have a strong positive linear association with the main cluster of data points. As mentioned earlier, a straight line is very close to these points. The closer they are to the same straight line, the stronger the correlation.
We have positive correlation because as x goes up, so does y. The variables increase together. Negative correlation happens when x and y go in opposite directions (one goes up, the other goes down).
The outlier point R pulls on the regression line to make it slightly not near the main cluster. The line tries its best to be near all of the points, and that includes the outlier. In some situations, the outlier is ignored and regression is done on the other set of points.
As you can probably guess, the outlier dilutes the strength of the correlation. The further the outlier, the weaker the correlation gets.