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 all the items against the STRESS measure to see which ones had the most impact on stress. Below are the findings: Model Summary Adjusted R Std. Error of R R Square Square the Estimate .742(a) .55 .53 1.39776 a Predictors: (Constant), REWARDS, LEARN, WORKLIFE, RESOURCES, INVOLVEMENT Model 1 ANOVA(b) Model 1 Regression Residual Total Sum of Squares 30.233 357.533 df 5 183 Mean Square 6.047 1.954 F 3.095 Sig. .015(a) 387.766 188 a Predictors: (Constant), REWARDS, LEARN, WORKLIFE, RESOURCES, INVOLVEMENT b Dependent Variable: STRESS Coefficients(a) Unstandardized Coefficients Model 1 Standardized Coefficients B 1.992 .158 Std. Error .560 .123 WORKLIFE .148 .125 INVOLVEMENT .292 LEARN .110 REWARDS .179 (Constant) RESOURCES Beta .117 t 3.558 1.287 Sig. .000 .060 .104 2.131 .030 .136 .200 2.147 .053 .112 .076 .978 .032 .134 .124 1.330 .055 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)