See data table in picture. If a male has a height of 66 in, a weight circumferen
ID: 3309235 • Letter: S
Question
See data table in picture.
If a male has a height of 66 in, a weight circumference of 101 cm, and a cholesterol level of 246 mg, what is the best predicted value of his weight? The table below was obtained by using data for 40 males. The response (y) variable is weight (in pounds), and the predictor (x) variables are HT (height in inches), WAIST(waist droumterence in am), and CHOL cholesterol in mg) Predictor (x) Variables HT, WAIST, CHOL HT, WAIST R2 0870 0.866 0.310 0 785 0.273 0790 0.001 199+23HT+22 -213 +2 09 HT+215 WAIST -135 + 4 65 HT +000674 CH OL -44.8+2 59 WAIST-0 0108 CHOL -139+4.55 HT 0859 0.850 199+2.3HT 22WAIST-0.1 CHOL 0.000 0.000 0.002 0.000 0.001 HT CHO WAIST, CHOL HT 0 773 0.254 0 785 0.000 -441+237 WAST CHOL 0 874 173-0.00233 CHOL it a male has a height of 66 in, a waist ciroumterence of 101 am and a cholesterol level of 246 mg what is the best predicted value of his wegh? The best predicted value of this male's weight is Round to the nearest whole number as needed.) Enter your answer in the answer AW parts showing
Explanation / Answer
Among all the equations given,the best equation to predict weight depending on all the informations given is the second equation.Because,if we look at the regression equation of weight on CHOL,we can clearly see that the p-value is 0.874>>>0.1.So,the model based on chol is insignificant.Also,if we check R^2 adjusted for the first two model,we can see that they are same.R^2 basically checks whether droppping or adding one variable,the the elationship is heavily changed or not.And,in this case,we can see that with the 2nd equation if we add CHOL variable,the adjusted R^2 is not increasing,which means that the model is not improving after adding the variable CHOL.So, we dont consider CHOL in regression.On the other hand,we see that HT have very poor effect on Y since R^2 is very low.Waist has moderate impact with R^2 adjusted equals 0.785 .But if we consider height and weight simultneously,adjusted R^2 is icreasing. So, we consider linear regression of weight on Height and Waist,which is given by, wt=-213+2.09*HT+2.15*Waist,now for Height=66in and Waist=101cm(you mentioned it as weight :D )
weight=-213+2.09*66+2.15*101=142