y: 3.423 3.242 3.385 3.420 3.380 3.402 x1: 8.5 8.5 8.5 8.5 8.5 8.5 X2: 60 40 40 40 40 40 y: 3.382 3.388 3.398 3.404 x1: 8.5 8.5 8.5 8.5 X2: 40 40 40 40 == a. A least squares fit of y = a + b₁x₁ + b₂x₂ to this data gave a = .0558, b₁ = .3749, and b₂ .0028. What value of deposition rate would you predict when wire feed rate = 11.5 and weld- ing speed 40? What is the value of the cor- responding residual? b. Residual and total sums of squares are .03836. and 5.1109, respectively. What proportion of observed variation in deposition rate can be attributed to the stated approximate relation- ship between deposition rate and the two pre- dictor variables? y: 2.718 3.881 2.773 3.924 2.740 3.870 17.0 10.0 7.0 10.0 7.0 10.0 X2: 30 30 50 50 30 30 y: 2.847 3.901 2.204 4.454 3.324 3.319 x1: 7.0 10.0 5.5 11.5 8.5 8.5 X2: 50 50 40 40 40 20
Recently there has been increased use of stainless steel claddings in industrial settings. Claddings are used to finish the exterior walls of a building and help weatherproof the structure. To ensure the quality of claddings, it is essential to know how welding parameters impact the cladding process. The authors of “Mathematical Modeling of Weld Bead Geometry, Quality, and Productivity for Stainless Steel Claddings Deposited by FCAW” (J. Mater. Engr. Perform., 2012: 1862–1872) in vestigated how y 5 deposition rate was influenced by x1 = feed rate (Wf , in m/min) and x2 = welding speed (S, in cm/min). The following 22 observations correspond to the experiment condition where applied voltage was less than 30v:
y: 2.718 3.881 2.773 3.924 2.740 3.870
x1 : 17.0 10.0 7.0 10.0 7.0 10.0 x 2 : 30 30 50 50 30 30
y: 2.847 3.901 2.204 4.454 3.324 3.319
x1 : 7.0 10.0 5.5 11.5 8.5 8.5
x2 : 50 50 40 40 40 20
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