Varieties of deprivation S1 (SES)

Effect of SES1 on blame

Unstandardized:

## 
## Call:
## lm(formula = "blame ~ SES1", data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2874 -0.2874  0.6996  0.7257  0.7648 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.23524    0.20699  25.292   <2e-16 ***
## SES1         0.01303    0.05793   0.225    0.822    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.031 on 135 degrees of freedom
## Multiple R-squared:  0.0003746,  Adjusted R-squared:  -0.00703 
## F-statistic: 0.05058 on 1 and 135 DF,  p-value: 0.8224

Standardized:

## 
## Call:
## lm(formula = scale(blame) ~ scale(SES1), data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1744 -0.2798  0.6812  0.7066  0.7446 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.724e-16  8.574e-02   0.000    1.000
## scale(SES1) 1.935e-02  8.605e-02   0.225    0.822
## 
## Residual standard error: 1.004 on 135 degrees of freedom
## Multiple R-squared:  0.0003746,  Adjusted R-squared:  -0.00703 
## F-statistic: 0.05058 on 1 and 135 DF,  p-value: 0.8224

Effect of SES2 on blame

Unstandardized:

## 
## Call:
## lm(formula = "blame ~ SES2", data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3288 -0.3288  0.5955  0.7469  0.9740 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   5.0260     0.2450  20.510   <2e-16 ***
## SES2          0.0757     0.0689   1.099    0.274    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.026 on 135 degrees of freedom
## Multiple R-squared:  0.008863,   Adjusted R-squared:  0.001521 
## F-statistic: 1.207 on 1 and 135 DF,  p-value: 0.2739

Standardized:

## 
## Call:
## lm(formula = scale(blame) ~ scale(SES2), data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2147 -0.3201  0.5798  0.7273  0.9484 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.701e-16  8.537e-02   0.000    1.000
## scale(SES2) 9.414e-02  8.568e-02   1.099    0.274
## 
## Residual standard error: 0.9992 on 135 degrees of freedom
## Multiple R-squared:  0.008863,   Adjusted R-squared:  0.001521 
## F-statistic: 1.207 on 1 and 135 DF,  p-value: 0.2739

Effect of SES (composite) on blame

Unstandardized:

## 
## Call:
## lm(formula = "blame ~ SES", data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3095 -0.3095  0.6459  0.7350  0.8685 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.13147    0.23697  21.655   <2e-16 ***
## SES          0.04452    0.06714   0.663    0.508    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.029 on 135 degrees of freedom
## Multiple R-squared:  0.003246,   Adjusted R-squared:  -0.004138 
## F-statistic: 0.4396 on 1 and 135 DF,  p-value: 0.5084

Standardized:

## 
## Call:
## lm(formula = scale(blame) ~ scale(SES), data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1960 -0.3014  0.6289  0.7156  0.8456 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.629e-16  8.561e-02   0.000    1.000
## scale(SES)  5.697e-02  8.593e-02   0.663    0.508
## 
## Residual standard error: 1.002 on 135 degrees of freedom
## Multiple R-squared:  0.003246,   Adjusted R-squared:  -0.004138 
## F-statistic: 0.4396 on 1 and 135 DF,  p-value: 0.5084