Least-squares criterion figures out the line that has the smallest sum of squared errors and is the one that fits the data best. The line that best fits a set of data points, according to the least-squares criterion, is known as the regression line. It is essential for making predictions. The regression equation is \(\displaystyle{y}={a}+{b}{x}\) where,

\(a=intercept\)

\(\displaystyle{b}={s}{l}{o}{p}{e}\)

\(x=independent\ variable\)

\(y=independent\ variable\)

The given equation is \(\displaystyle{w}{e}{i}{g}{h}{t}={0.25}+{0.61}{h}{r}{s}\)

The value of x is 3. \(\displaystyle{w}{e}{i}{g}{h}{t}={0.25}+{0.61}{h}{r}{s}\)

\(\displaystyle={0.25}+{0.61}\cdot{3}\)

\(\displaystyle={0.25}+{1.83}\)

\(\displaystyle-{2.08}\)

Therefore the b part is correct.