Perform a regression proc reg data=work.import; model charges = bmi; where type EQ 'train'; /* Use only the 'train' data */ run; Use Excel to fill in PRED This uses the IF() command. =IF([@TYPE]="pred",[@CHARGES],NA()) Operationalizations Define the variables appearing in the data set. AGE is the age of the person/case. BMI is the body mass index of the person/case. CHARGES is the dollar amount (the cost) of the medical procedure for the person/case. TYPE is the variable assigning the person/case to training SAS. · Assignment of train is used for training SAS. · Assignment of pred will be used to predict within Excel. TRAIN is used to plot CHARGES (for training data) in Excel. PRED is used to plot predicted CHARGES in Excel. Data: 27 42.13 $39,611.76 train 39611.7577 #N/A 19 24.6 $1,837.24 train 1837.237 #N/A 52 30.78 $10,797.34 train 10797.3362 #N/A 23 23.845 $2,395.17 train 2395.17155 #N/A 56 40.3 $10,602.39 train 10602.385 #N/A 30 35.3 $36,837.47 train 36837.467 #N/A 60 36.005 $13,228.85 train 13228.847 #N/A 30 32.4 $4,149.74 train 4149.736 #N/A 18 34.1 $1,137.01 train 1137.011 #N/A 34 31.92 $37,701.88 train 37701.8768 #N/A 37 28.025 $6,203.90 train 6203.90175 #N/A 59 27.72 $14,001.13 train 14001.1338 #N/A 63 23.085 $14,451.84 train 14451.8352 #N/A 55 32.775 $12,268.63 train 12268.6323 #N/A 23 17.385 $2,775.19 train 2775.19215 #N/A 31 36.3 $38,711.00 train 38711 #N/A 22 35.6 $35,585.58 train 35585.576 #N/A 18 26.315 $2,198.19 train 2198.18985 #N/A 19 28.6 $4,687.80 train 4687.797 #N/A 63 28.31 $13,770.10 train 13770.0979 #N/A 28 36.4 $51,194.56 train 51194.5591 #N/A 19 20.425 $1,625.43 train 1625.43375 #N/A 62 32.965 $15,612.19 train 15612.1934 #N/A 26 20.8 $2,302.30 train 2302.3 #N/A
Perform a regression |
proc reg data=work.import; model charges = bmi; where type EQ 'train'; /* Use only the 'train' data */ run; |
Use Excel to fill in PRED
This uses the IF() command. |
=IF([@TYPE]="pred",[@CHARGES],NA()) |
Operationalizations
Define the variables appearing in the data set. |
AGE is the age of the person/case. BMI is the body mass index of the person/case. CHARGES is the dollar amount (the cost) of the medical procedure for the person/case. TYPE is the variable assigning the person/case to training SAS. · Assignment of train is used for training SAS. · Assignment of pred will be used to predict within Excel. TRAIN is used to plot CHARGES (for training data) in Excel. PRED is used to plot predicted CHARGES in Excel. |
Data:
27 | 42.13 | $39,611.76 | train | 39611.7577 | #N/A |
19 | 24.6 | $1,837.24 | train | 1837.237 | #N/A |
52 | 30.78 | $10,797.34 | train | 10797.3362 | #N/A |
23 | 23.845 | $2,395.17 | train | 2395.17155 | #N/A |
56 | 40.3 | $10,602.39 | train | 10602.385 | #N/A |
30 | 35.3 | $36,837.47 | train | 36837.467 | #N/A |
60 | 36.005 | $13,228.85 | train | 13228.847 | #N/A |
30 | 32.4 | $4,149.74 | train | 4149.736 | #N/A |
18 | 34.1 | $1,137.01 | train | 1137.011 | #N/A |
34 | 31.92 | $37,701.88 | train | 37701.8768 | #N/A |
37 | 28.025 | $6,203.90 | train | 6203.90175 | #N/A |
59 | 27.72 | $14,001.13 | train | 14001.1338 | #N/A |
63 | 23.085 | $14,451.84 | train | 14451.8352 | #N/A |
55 | 32.775 | $12,268.63 | train | 12268.6323 | #N/A |
23 | 17.385 | $2,775.19 | train | 2775.19215 | #N/A |
31 | 36.3 | $38,711.00 | train | 38711 | #N/A |
22 | 35.6 | $35,585.58 | train | 35585.576 | #N/A |
18 | 26.315 | $2,198.19 | train | 2198.18985 | #N/A |
19 | 28.6 | $4,687.80 | train | 4687.797 | #N/A |
63 | 28.31 | $13,770.10 | train | 13770.0979 | #N/A |
28 | 36.4 | $51,194.56 | train | 51194.5591 | #N/A |
19 | 20.425 | $1,625.43 | train | 1625.43375 | #N/A |
62 | 32.965 | $15,612.19 | train | 15612.1934 | #N/A |
26 | 20.8 | $2,302.30 | train | 2302.3 | #N/A |
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