Varieties of deprivation S1 (SES)
Date of study: Apr 9 2024. Last compiled on 03 July, 2024
Effect of SES1 on blame
Unstandardized:
##
## Call:
## lm(formula = "blame ~ SES1", data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2303 -0.4334 0.3634 0.7697 1.1760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.82402 0.12617 38.234 < 2e-16 ***
## SES1 0.13543 0.03375 4.013 7.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 273 degrees of freedom
## Multiple R-squared: 0.05569, Adjusted R-squared: 0.05223
## F-statistic: 16.1 on 1 and 273 DF, p-value: 7.763e-05Standardized:
##
## Call:
## lm(formula = scale(blame) ~ scale(SES1), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8462 -0.4016 0.3367 0.7132 1.0896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.467e-16 5.871e-02 0.000 1
## scale(SES1) 2.360e-01 5.881e-02 4.013 7.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9735 on 273 degrees of freedom
## Multiple R-squared: 0.05569, Adjusted R-squared: 0.05223
## F-statistic: 16.1 on 1 and 273 DF, p-value: 7.763e-05
Effect of SES2 on blame
Unstandardized:
##
## Call:
## lm(formula = "blame ~ SES2", data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0383 -0.5563 0.3070 0.6164 1.3070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.69301 0.13877 33.818 < 2e-16 ***
## SES2 0.17265 0.03756 4.597 6.57e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.042 on 273 degrees of freedom
## Multiple R-squared: 0.07184, Adjusted R-squared: 0.06844
## F-statistic: 21.13 on 1 and 273 DF, p-value: 6.57e-06Standardized:
##
## Call:
## lm(formula = scale(blame) ~ scale(SES2), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6684 -0.5154 0.2844 0.5711 1.2110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.021e-16 5.820e-02 0.000 1
## scale(SES2) 2.680e-01 5.831e-02 4.597 6.57e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9652 on 273 degrees of freedom
## Multiple R-squared: 0.07184, Adjusted R-squared: 0.06844
## F-statistic: 21.13 on 1 and 273 DF, p-value: 6.57e-06
Effect of SES (composite) on blame
Unstandardized:
##
## Call:
## lm(formula = "blame ~ SES", data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1381 -0.4621 0.3760 0.6999 1.2668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.73324 0.13498 35.067 < 2e-16 ***
## SES 0.16196 0.03658 4.428 1.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.044 on 273 degrees of freedom
## Multiple R-squared: 0.067, Adjusted R-squared: 0.06358
## F-statistic: 19.6 on 1 and 273 DF, p-value: 1.38e-05Standardized:
##
## Call:
## lm(formula = scale(blame) ~ scale(SES), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7608 -0.4281 0.3484 0.6485 1.1737
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.386e-16 5.835e-02 0.000 1
## scale(SES) 2.588e-01 5.846e-02 4.428 1.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9677 on 273 degrees of freedom
## Multiple R-squared: 0.067, Adjusted R-squared: 0.06358
## F-statistic: 19.6 on 1 and 273 DF, p-value: 1.38e-05