Incorrect Choices

All data analysis performed on SPSS , a joint venture partner with LWI.

		Setting A	Setting B	Output
Setting A	Pearson Correlation	1000	.066	-.142
	Sig. (2-tailed)	.	.685	.381
	N	40	40	40
Setting B	Pearson Correlation	.066	1.000	.015
	Sig. (2-tailed)	.685	.	.927
	N	40	40	40
Output	Pearson Correlation	-.142	.015	1.000
	Sig. (2-tailed)	.381	.927	.
	N	40	40	40

The correlation table here says that there is no significant correlation between Setting A, Setting B, and the Output.

Linear Regression Analysis

This table shows that a linear regression model only explains about 2% of the variability of Output.

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.144^a	.021	-.032	1.8476

^a Predictors: (Constant), Setting B, Setting A

ANOVA^b

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	2.688	2	1.344	.394	.677^a
	Residual	126.309	37	3.414
	Total	128.997	39

^aPredictors: (Constant), Setting B, Setting A
^bDependent Variable: Output

This table shows that the regression model of Setting A and Setting B does not significantly explain the variability in Output.

This table gives the constants for the linear regression, and again tells you that they are not significant. Only the constant in the equation is significant which implies that the average of all the Outputs is the best estimate of what your Output will be for any Setting A and Setting B.

Coefficients^a

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	15.627	1.239		12.615	.000
	Setting A	-5.08E-02	.058	-.144	-.883	.383
	Setting B	8.166E-03	.055	.024	.150	.882

^aDependent Variable: Output