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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 Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.074a
.005
.004
.838
a. Predictors: (Constant), Male Dummy Variable, White Dummy Variable
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
4.340
2
2.170
3.091
.046b
Residual
797.495
1136
.702
Total
801.835
1138
a. Dependent Variable: Recoded – Days of Poor Mental Health
b. Predictors: (Constant), Male Dummy Variable, White Dummy Variable
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
1.650
.056
29.298
.000
White Dummy Variable
.134
.057
.069
2.329
.020
Male Dummy Variable
-.056
.051
-.033
-1.103
.270
a. 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 person’s 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 someone’s gender allows for 3.3% increase in ability to predict someone’s 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 person’s days of poor mental health days in a 30day scope with any certainty.
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