3. The organization that surveyed the employees wanted to know what factors influenced STRESS so they could take action. To do that a multiple regression analysis was run that regressed the all the items against the STRESS measure to see which ones had the most impact on stress. Below are the findings:

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.742(a)	.58	.55	1.39776

a Predictors: (Constant), REWARDS, LEARN, WORKLIFE, RESOURCES, INVOLVEMENT

ANOVA(b)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	30.233	5	6.047	3.095	.015(a)
Residual	357.533	183	1.954
Total	387.766	188

a Predictors: (Constant), REWARDS, LEARN, WORKLIFE, RESOURCES, INVOLVEMENT

b Dependent Variable: STRESS

Coefficients(a)

Model		Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1
(Constant)	1.992	.560		3.558	.000
RESOURCES	.158	.123	.117	1.287	.060
WORKLIFE	.148	.125	.104	2.131	.040
INVOLVEMENT	.292	.136	.200	2.147	.058
LEARN	.110	.112	.076	.978	.032
REWARDS	.179	.134	.124	1.330	.071

a Dependent Variable: STRESS

What is the R-square and what does it mean? (1 pt)

Was the overall multiple regression significant? (1 pt)

Looking at each regression coefficient, which one(s), if any, are statistically significant? (1 pt)

Summarize the results in 2-3 sentences. (1 pt)