314 Asg 2 Q1.pdf
Transcript of 314 Asg 2 Q1.pdf
Y X1 X2 X3250 35 3 6360 29 4 10165 36 7 343 60 6 992 65 5 6200 30 5 5355 10 6 7290 7 10 10230 21 9 11120 55 2 573 54 12 4205 48 5 1400 20 5 15300 25 3 1380 18 10 2210 32 13 1760 10 9 6620 12 8 12550 16 4 15305 25 5 580 38 7 460 42 5 2150 30 6 2780 9 7 6820 8 1 8
Regression Analysis: Y versus X1, X2, X3
The regression equation isY = 664 - 10.3 X1 - 12.6 X2 + 5.01 X3
Predictor Coef SE Coef T PConstant 663.7 120.0 5.53 0.000X1 -10.338 1.857 -5.57 0.000X2 -12.59 10.08 -1.25 0.226X3 5.007 7.746 0.65 0.525
S = 143.813 R-Sq = 65.8% R-Sq(adj) = 61.0%
Analysis of Variance
Source DF SS MS F PRegression 3 837065 279022 13.49 0.000
Residual Error 21 434325 20682Total 24 1271390
Source DF Seq SSX1 1 788120X2 1 40304X3 1 8641
Unusual Observations
Obs X1 Y Fit SE Fit Residual St Resid17 10.0 760.0 477.0 51.0 283.0 2.10R
R denotes an observation with a large standardized residual.
The regression equation is Y = 664 - 10.3 X1 - 12.6 X2 + 5.01 X3. We note that heating cost is inversely related to mean outside temperature and attic insulation, while it is positively related to age of furnace which makes sense. Higher the outside temperature, less amount of heat is needed. Thicker the attic insulation, lesser the heat lost, so less amount of heat needs to be generated. Also the older the furnace, the less effective and efficient it is, so we should except more cost of heat production if furnace is older; hence positive relation.