Variables Entered/Removed Entered Removed Method Enter Enter Model a. Dependent
ID: 3293524 • Letter: V
Question
Variables Entered/Removed Entered Removed Method Enter Enter Model a. Dependent Variable: perceived.threat b. All requested variables entered Model Summary Change Statistics Adjusted R Square Std. Error of the Estimate 22.56689 21.26908 quare Change Model R Square F Change dri dr2 Change Watson 002 409 59* 345 116 25.649 195 a. Predictors: (Constant), age b. Predictors: (Constant), age, anger c. DependentVaiable: perceived threat ANOVA Sum of Squares Mean Square 348.079 509.265 Regression Residual Total Regression Residual Total 409 348.079 99B15,870 100163.949 1951.100 88212.849 00163.949 683 975.550 13.209 452.374 197 a Dependent Variable: perceivedthreat b Predictors (Constant), age c. Predictors: (Constan), age, anger Coefficients Standardized Coefficients Unstandardized Coefficients Collinearity Statistics Model Std. Error Si olerance (Constant) age Constant) 17.485 332 5.175 10.327 402 10.713 379 894 1.693 827 483 1.007 5.065 059 409 1.000 1.000 630 068 340 nger 4.526 1.001 a. Dependent Variable: perceived.threat Excluded Variables Collinearity Statistics Partial Correlation Tolerance Minimum Tolerance Si anger 340 a. Dependent Variable: perceived.threat b. Predictors in the Model: (Constant), ageExplanation / Answer
1)
Ho: coefficient of anger is not significant. Beta 2 = 0
h1: coefficient of anger is significant. beta2 =/= 0
With t = 5.065 and p-value < 0.05, I reject ho and conclude that coefficient of anger is significant. beta2 =/= 0
Also value of adj R^2 increases with addition of significant variables. adj R^2 for model 2 is greater than that of model 1, hence anger will help us better to predict threat level .
2)
i)
from residual plot or normal probability plot I can say that residuals are normally distributed.
ii)
with VIF < 3, I can say that there is no problem of multi-co-linearity associated with the data. And hence independence of variables.
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