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age gender bmi children smoker 1 18.000000 0.000000 33.800000 1.000000 0.000000 2 28.000000 0.000000 33.000000 3.000000 0.000000 3 31.000000 1.000000 25.700000 0.000000 0.000000 4 46.000000

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age gender bmi children smoker 1 18.000000 0.000000 33.800000 1.000000 0.000000 2 28.000000 0.000000 33.000000 3.000000 0.000000 3 31.000000 1.000000 25.700000 0.000000 0.000000 4 46.000000 1.000000 33.400000 1.000000 0.000000 5 62.000000 1.000000 26.300000 0.000000 1.000000 6 56.000000 1.000000 39.800000 0.000000 0.000000 7 27.000000 0.000000 42.100000 0.000000 1.000000 8 18.000000 0.000000 34.100000 0.000000 0.000000 9 59.000000 1.000000 27.700000 3.000000 0.000000 10 31.000000 1.000000 36.600000 2.000000 0.000000 11 41.000000 0.000000 21.800000 1.000000 0.000000 12 37.000000 1.000000 30.800000 2.000000 0.000000 13 60.000000 1.000000 24.500000 0.000000 0.000000 14 36.000000 0.000000 35.200000 1.000000 1.000000 15 36.000000 0.000000 34.400000 0.000000 1.000000 16 18.000000 0.000000 31.700000 2.000000 1.000000 17 53.000000 1.000000 22.900000 1.000000 1.000000 18 25.000000 0.000000 33.700000 4.000000 0.000000 19 40.000000 1.000000 36.200000 0.000000 0.000000 20 28.000000 0.000000 24.000000 3.000000 1.000000 21 27.000000 1.000000 24.800000 0.000000 1.000000 22 59.000000 0.000000 32.000000 1.000000 0.000000 23 29.000000 1.000000 29.600000 1.000000 0.000000 24 21.000000 0.000000 35.500000 0.000000 0.000000 25 22.000000 0.000000 37.600000 1.000000 1.000000 7 27.000000 0.000000 42.100000 0.000000 1.000000 8 18.000000 0.000000 34.100000 0.000000 0.000000 9 59.000000 1.000000 27.700000 3.000000 0.000000 10 31.000000 1.000000 36.600000 2.000000 0.000000 11 41.000000 0.000000 21.000000 1.000000 0.000000 12 37.000000 1.000000 30.800000 2.000000 0.000000 13 60.000000 1.000000 24.500000 0.000000 0.000000 14 36.000000 0.000000 35.200000 1.000000 1.000000 15 36.000000 0.000000 34.400000 0.000000 1.000000 16 18.000000 0.000000 31.700000 2.000000 1.000000 17 53.000000 1.000000 22.900000 1.000000 1.000000 4.000000 18 25.000000 0.000000 33.700000 0.000000 19 40.000000 1.000000 36.200000 0.000000 0.000000 20 28.000000 0.000000 24.000000 3.000000 1.000000 21 27.000000 1.000000 24.800000 0.000000 1.000000 22 58.000000 0.000000 32.000000 1.000000 0.000000 23 29.000000 1.000000 29.600000 1.000000 0.000000 24 21.000000 0.000000 35.500000 0.000000 0.000000 25 22.000000 0.000000 37.600000 1.000000 1.000000 26 21.000000 1.000000 39.500000 0.000000 0.000000 27 28.000000 1.000000 37.600000 1.000000 0.000000 28 55.000000 0.000000 38.300000 0.000000 0.000000 29 61.000000 1.000000 29.900000 3.000000 1.000000 30 29.000000 0.000000 27.900000 0.000000 0.000000 ... How strong of a correlation is there between expenses and the variables of age, gender, bmi, children, and smoker? Which variable is the most significant and useful driver in predicting the expenses incurred during the year? I Is there any concern with the validity of the regression model that uses expenses and the most significant cost driver? age gender bmi children smoker 1 18.000000 0.000000 33.800000 1.000000 0.000000 2 28.000000 0.000000 33.000000 3.000000 0.000000 3 31.000000 1.000000 25.700000 0.000000 0.000000 4 46.000000 1.000000 33.400000 1.000000 0.000000 5 62.000000 1.000000 26.300000 0.000000 1.000000 6 56.000000 1.000000 39.800000 0.000000 0.000000 7 27.000000 0.000000 42.100000 0.000000 1.000000 8 18.000000 0.000000 34.100000 0.000000 0.000000 9 59.000000 1.000000 27.700000 3.000000 0.000000 10 31.000000 1.000000 36.600000 2.000000 0.000000 11 41.000000 0.000000 21.800000 1.000000 0.000000 12 37.000000 1.000000 30.800000 2.000000 0.000000 13 60.000000 1.000000 24.500000 0.000000 0.000000 14 36.000000 0.000000 35.200000 1.000000 1.000000 15 36.000000 0.000000 34.400000 0.000000 1.000000 16 18.000000 0.000000 31.700000 2.000000 1.000000 17 53.000000 1.000000 22.900000 1.000000 1.000000 18 25.000000 0.000000 33.700000 4.000000 0.000000 19 40.000000 1.000000 36.200000 0.000000 0.000000 20 28.000000 0.000000 24.000000 3.000000 1.000000 21 27.000000 1.000000 24.800000 0.000000 1.000000 22 59.000000 0.000000 32.000000 1.000000 0.000000 23 29.000000 1.000000 29.600000 1.000000 0.000000 24 21.000000 0.000000 35.500000 0.000000 0.000000 25 22.000000 0.000000 37.600000 1.000000 1.000000 7 27.000000 0.000000 42.100000 0.000000 1.000000 8 18.000000 0.000000 34.100000 0.000000 0.000000 9 59.000000 1.000000 27.700000 3.000000 0.000000 10 31.000000 1.000000 36.600000 2.000000 0.000000 11 41.000000 0.000000 21.000000 1.000000 0.000000 12 37.000000 1.000000 30.800000 2.000000 0.000000 13 60.000000 1.000000 24.500000 0.000000 0.000000 14 36.000000 0.000000 35.200000 1.000000 1.000000 15 36.000000 0.000000 34.400000 0.000000 1.000000 16 18.000000 0.000000 31.700000 2.000000 1.000000 17 53.000000 1.000000 22.900000 1.000000 1.000000 4.000000 18 25.000000 0.000000 33.700000 0.000000 19 40.000000 1.000000 36.200000 0.000000 0.000000 20 28.000000 0.000000 24.000000 3.000000 1.000000 21 27.000000 1.000000 24.800000 0.000000 1.000000 22 58.000000 0.000000 32.000000 1.000000 0.000000 23 29.000000 1.000000 29.600000 1.000000 0.000000 24 21.000000 0.000000 35.500000 0.000000 0.000000 25 22.000000 0.000000 37.600000 1.000000 1.000000 26 21.000000 1.000000 39.500000 0.000000 0.000000 27 28.000000 1.000000 37.600000 1.000000 0.000000 28 55.000000 0.000000 38.300000 0.000000 0.000000 29 61.000000 1.000000 29.900000 3.000000 1.000000 30 29.000000 0.000000 27.900000 0.000000 0.000000 ... How strong of a correlation is there between expenses and the variables of age, gender, bmi, children, and smoker? Which variable is the most significant and useful driver in predicting the expenses incurred during the year? I Is there any concern with the validity of the regression model that uses expenses and the most significant cost driver

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