Write a respond to the below post: As a reminder to the class my study topic is the correlation between social class and the instance of poor mental health days in the scope of a 30 day period. After creating the dummy variables for race & gender and running the Linear Regression model as depicted in the text these were my results in tables: Model SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.074a.005.004.838a. Predictors: (Constant), Male Dummy Variable, White Dummy VariableANOVAaModelSum of SquaresdfMean SquareFSig.1Regression4.34022.1703.091.046bResidual797.4951136.702Total801.8351138 a. Dependent Variable: Recoded Days of Poor Mental Healthb. Predictors: (Constant), Male Dummy Variable, White Dummy VariableCoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)1.650.056 29.298.000White Dummy Variable.134.057.0692.329.020Male Dummy Variable-.056.051-.033-1.103.270a. Dependent Variable: Recoded Days of Poor Mental Health According to the output data, our white dummy variable shows us that we are more likely to predict the days of poor mental health when knowing a persons race by 6.9% which is a weak association. The significance level of this information is significant at the .02 level because 0.02< 0.05. Knowing someones gender allows for 3.3% increase in ability to predict someones days of poor mental health which is very weak and lacks in a valid significance score at 0.27. The overall significance score of regression in the ANOVA model shows that our overall significance score is 0.046 which is less than the standard 0.05 therefore it is significant. The first table shows that the independent variables explain 0.5% of the variability in the dependent variable (R Square). This means the relation is far too weak. Neither race nor gender has a strong enough correlation to predict a persons days of poor mental health days in a 30day scope with any certainty.
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