Which statement(s) are correct for the Regression Analysis shown here? (Note: There are 2 correct answers). Regression Analysis: HeatFlux versus \%Cu, Thickness The Regression Equation is HeatFlux =484+4.80 %Cu -24.2 Thickness predictor Coef SE Coef T P Constant 483.67 39.57 12.22 0.000 %Cu 4.7963 0.9511 5.04 0.000 Thickness -24.215 1.941 -12.48 0.000 {\S}=8.93207 {\R}-{\Sq}=85.9% {\R}-{\Sq}({\adj})=84.8% Analysis of Variance Source DF SS MS F P Regression 2 12607.6 6303.8 79.01 0.000 Residual Error 26 2074.3 79.8 Total 28 14681.9 Source DF Seq SS %Cu 1 184.5 Thickness 1 12423.1 Unusual observations Obs %Cu Heat Flux Fit SE Fit Residual St Resid 1 40.6 271.80 274.74 5.08 -2.94 -0.40X 22 36.3 254.50 230.91 2.39 23.59 2.74R R denotes an observation with a large standardized residual. {X} denotes an observation whose {X} value gives it large influence.c

This Regression is an example of a Multiple Linear Regression.

This Regression is an example of Cubic Regression.

%Cu explains the majority of the process variance in heat flux

D. Thickness explains over 80% of the process variance in heat flux.

The number of Residuals in this Regression Analysis is 26.

Six Sigma Iassc Green Belt Yellow Belt Certification Questions Part 1

Question : 6 of 30

Marks: +1, -0

Which statement(s) are correct for the Regression Analysis shown here? (Note: There are 2 correct answers).
Regression Analysis: HeatFlux versus \%Cu, Thickness
The Regression Equation is
HeatFlux

=484+4.80

%Cu

-24.2

Thickness

predictor	Coef	SE Coef	T
Constant	483.67	39.57	12.22
%Cu	4.7963	0.9511	5.04
Thickness	-24.215	1.941	-12.48

{\S}=8.93207 \;\;\;\;{\R}-{\Sq}=85.9%

{\R}-{\Sq}({\adj})=84.8%

Analysis of Variance

Source	DF	SS	MS	F	P
Regression	2	12607.6	6303.8	79.01	0.000
Residual Error	26	2074.3	79.8
Total	28	14681.9

Source	DF	Seq SS
%Cu	1	184.5
Thickness	1	12423.1

Unusual observations

Obs	%Cu	Heat Flux	Fit	SE Fit	Residual	St Resid
1	40.6	271.80	274.74	5.08	-2.94	-0.40X
22	36.3	254.50	230.91	2.39	23.59	2.74R

R denotes an observation with a large standardized residual.

{X}

denotes an observation whose

{X}

value gives it large influence.c

This Regression is an example of a Multiple Linear Regression.
This Regression is an example of Cubic Regression.
%Cu explains the majority of the process variance in heat flux
D. Thickness explains over 80% of the process variance in heat flux.
The number of Residuals in this Regression Analysis is 26.

Validate

Go to Question:

Prev Question

Next Question