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Multiple regression equation for BMI data.

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The response (y) variable is weight in pounds, and the predictor (x) variables are HT (height in inches), WAIST (waist circumference in cm) and CHOL (cholesterol in mg).

Predictor (x) Variables P-Value R² Adjusted R² Regression Equation
HT, WAIST, CHOL 0.000 0.880 0.870 Y = -199 + 2.55 HT + 2.18 WAIST - 0.00534 CHOL
HT, WAIST 0.000 0.877 0.870 Y = -206 + 2.66 HT + 2.15 WAIST
HT, CHOL 0.002 0.277 0.238 Y = -148 + 4.65 HT + 0.00589 CHOL
WAIST, CHOL 0.000 0.804 0.793 Y = -42.8 + 2.41 WAIST - 0.0106 CHOL
HT 0.001 0.273 0.254 Y = -139 + 4.55 HT
WAIST 0.000 0.790 0.785 Y = -44.1 + 2.37 WAIST
CHOL 0.874 0.001 0.000 Y = 173 - 0.00233 CHOL

1. If exactly two predictor (x) variables are to be used to predict weight, which two variables should be chosen and why?

2. If a male has a height of 72in., a weight circumference of 105cm, and a cholesterol level of 250 mg, what is the best predicted value of his weight? Is that predicted value likely to be a good estimate? Is that predicated value likely to be very accurate?

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The solution give the details of multiple regression analysis of BMI data. Regression coefficients, significance test procedure, R square are given with interpretations.

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The response (y) variable is weight in pounds, and the predictor (x) variables are HT (height in inches), WAIST (waist circumference in cm) and CHOL (cholesterol in mg).

Predictor (x) Variables P-Value R² Adjusted R² Regression Equation
HT, WAIST, CHOL 0.000 0.880 0.870 Y = -199 + 2.55 HT + 2.18 WAIST - 0.00534 CHOL
HT, WAIST 0.000 0.877 0.870 Y = ...

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