Varieties of deprivation S1 (SES)
Date of study: Apr 9 2024. Last compiled on 22 July, 2024
Effect of SES1 on blame
Unstandardized:
##
## Call:
## lm(formula = "blame ~ SES1", data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2012 -0.5908 0.2144 0.4092 1.3832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.61684 0.16060 28.747 < 2e-16 ***
## SES1 0.19479 0.04123 4.725 5.68e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.053 on 136 degrees of freedom
## Multiple R-squared: 0.141, Adjusted R-squared: 0.1347
## F-statistic: 22.32 on 1 and 136 DF, p-value: 5.679e-06Standardized:
##
## Call:
## lm(formula = scale(blame) ~ scale(SES1), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5938 -0.5218 0.1894 0.3614 1.2216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.815e-16 7.919e-02 0.000 1
## scale(SES1) 3.755e-01 7.947e-02 4.725 5.68e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9302 on 136 degrees of freedom
## Multiple R-squared: 0.141, Adjusted R-squared: 0.1347
## F-statistic: 22.32 on 1 and 136 DF, p-value: 5.679e-06
Effect of SES2 on blame
Unstandardized:
##
## Call:
## lm(formula = "blame ~ SES2", data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9778 -0.6132 0.2341 0.5986 1.4459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.55411 0.17244 26.409 < 2e-16 ***
## SES2 0.21182 0.04506 4.701 6.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.054 on 136 degrees of freedom
## Multiple R-squared: 0.1398, Adjusted R-squared: 0.1334
## F-statistic: 22.1 on 1 and 136 DF, p-value: 6.278e-06Standardized:
##
## Call:
## lm(formula = scale(blame) ~ scale(SES2), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3965 -0.5416 0.2067 0.5287 1.2770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.410e-16 7.924e-02 0.000 1
## scale(SES2) 3.739e-01 7.953e-02 4.701 6.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9309 on 136 degrees of freedom
## Multiple R-squared: 0.1398, Adjusted R-squared: 0.1334
## F-statistic: 22.1 on 1 and 136 DF, p-value: 6.278e-06
Effect of SES (composite) on blame
Unstandardized:
##
## Call:
## lm(formula = "blame ~ SES", data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0893 -0.6129 0.2536 0.4343 1.4343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.56570 0.16765 27.23 < 2e-16 ***
## SES 0.20944 0.04363 4.80 4.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 136 degrees of freedom
## Multiple R-squared: 0.1449, Adjusted R-squared: 0.1386
## F-statistic: 23.04 on 1 and 136 DF, p-value: 4.114e-06Standardized:
##
## Call:
## lm(formula = scale(blame) ~ scale(SES), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4950 -0.5413 0.2240 0.3836 1.2668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.586e-17 7.901e-02 0.0 1
## scale(SES) 3.806e-01 7.929e-02 4.8 4.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9281 on 136 degrees of freedom
## Multiple R-squared: 0.1449, Adjusted R-squared: 0.1386
## F-statistic: 23.04 on 1 and 136 DF, p-value: 4.114e-06