This post covers Introduction to probability from Statistics for Engineers and Scientists by William Navidi.

Basic Ideas

• A study is done in which a sample of men were weighed, and then each man was tested to see how much weight he could lift. The explanatory variable $(x)$ was the man’s weight, and the outcome $(y)$ was the amount he could lift.

  The least-squares line was computed to be $y = 50 + 0.6x$. Joe is a weightlifter. After looking at the equation of the least-squares regression line, he reasons as follows: “The slope of the least-squares regression line is $0.6$”.

  Therefore, if I gain $10$ pounds, I’ll be able to lift $6$ pounds more, because $(0.6)(10) = 6$.” Is he right?