Heights (in centimeters) and weights (in kilograms) of 7 supermodels are given below. Find the regression equation, letting the first variable be the independent (x) variable, and predict the weight of a supermodel who is 173 cm tall.
\begin{array}{c|ccccccc} \mbox{Height} & 174 & 166 & 176 & 176 & 174 & 176 & 168 \cr \hline \mbox{Weight} & 54 & 47 & 56 & 55 & 55 & 54 & 50 \cr \end{array}

Question

Heights (in centimeters) and weights (in kilograms) of 7 supermodels are given below. Find the regression equation, letting the first variable be the independent (x) variable, and predict the weight of a supermodel who is 173 cm tall.
\begin{array}{c|ccccccc} \mbox{Height} & 174 & 166 & 176 & 176 & 174 & 176 & 168 \cr \hline \mbox{Weight} & 54 & 47 & 56 & 55 & 55 & 54 & 50 \cr \end{array}

anonymous · Answer

@badreferences

dumbcow · Answer

can you use a graphing calculator?
just input the data into 2 lists, then calc linear regression

anonymous · Answer

which would be the x? height or weight?

dumbcow · Answer

height

dumbcow · Answer

go to "Stat" then "Calc", "LinReg"
im using a TI83

anonymous · Answer

@Ishaan94 Hello. XD @dumbcow 's got it, though.

dumbcow · Answer

you could also use Excel by creating a chart and adding a trendline

anonymous · Answer

y = .7583x + 78.083 is a linear function, but what is R^2 = .9242?

dumbcow · Answer

its a measure of goodness of fit, in other words it says how close the line fits the actual data
0- worst
1 - best

anonymous · Answer

R^2 is the coefficient of determination, which tells us how well the line function fits within the general variability of the data.

Or what @dumcow said. It's minimum is 0 for 0%; it's max is 1 for 100%.

dumbcow · Answer

:)