Smokers don’t live as long (on average) as nonsmokers, and heavy smokers don’t live as long as light smokers. You perform least-squares regression on the age at death of a group of male smokers y and the number of packs per day they smoked x. The slope of your regression line

(a) will be greater than $0$

(b) will be less than $0$

(c) will be equal to$0$

(d) You can’t perform regression on these data.

(e) You can’t tell without seeing the data.

You run a least-squares regression on a group of male smokers' age at death $y$and the number of packs per day $x$

A regression line is a straight line that depicts the change in a response variable $y$ when an explanatory variable $x$ changes. By putting this $x$ into the equation of the line, you may use a regression line to predict the value of $y$ for any value of $x$

Smokers (on average) have shorter lives than nonsmokers, and heavy smokers live shorter lives than light smokers. Assume you're conducting a least-square regression experiment on a sample of male smokers' death ages,$Y$, and the number of packs per day they smoked, $X$The age number of survival days reduces as the number of packs smoked each day increases. In other words, as $X$increases, $Y$decreases. To put it another way, $X$and $Y$have an antagonistic connection. We know that the sign of the slope and the sign of the correlation coefficient are the same. As a result, we can say that the slope of the regression line is negative. Hence, the slope of the regression line will be negative, which is less than $0$