Model summary Linear Mixed Models ! + 0 X Deviance Deviance (REML) log Lik. df AIC BIC income_reg Dependent variable 6242.3468 6203.1090 -3101.5545 11 6225. 1090 6265.8506 cent_income_reg income Note. The model was fitted using restricted maximum likelihood. equality_reg Fixed effects variables studentStaffRatio_sch cent_academicAch . Fixed Effects Estimates prestige_sch cent_intelligence anxiety Term Estimate SE df t P anxiety.categ Intercept 80365.4136 514.8966 287.7628 156.0807 3.8535e-280 anxiety.ordinal cent_academicAch 1049.0509 97.3880 289.6476 10.7719 5.6099e -23 cent_anxiety cent_intelligence -19.5673 48.5571 289.8136 -0.4030 0.6873 cent_academicAch * cent_intelligence 1.9869 4.9466 295.9734 0.4017 0.6882 personality.agr cent_personality.agr Random effects grouping factors Note. The intercept corresponds to the (unweighted) grand mean; for each factor with k levels, k - 1 parameters are estimated. Consequently, the estimates cannot be directly mapped to factor levels. personality.con de region cent_personality.con personality.ext Variance/Correlation Estimates cent_personality.ext intelligence region: Variance Estimates teach Qual Term Std. Deviation Variance Intercept 6.9639 48.4961 Model cent_academicAch 1.2798 1.6380 cent_intelligence 0.5624 0.3163 Model components Fixed effects Note. The intercept corresponds to the (unweighted) cent_academicAch 12 cent_academicAch grand mean; for each factor with k levels, k - 1 cent_intelligence cent_intelligence parameters are estimated. Consequently, the estimates cannot be directly mapped to factor levels. cent_academicAch * cent_intellige . . region: Correlation Estimates Term ntercept cent_academicAch cent_intelligence Intercept 1.0000 cent_academicAch -0.0141 1.0000 cent_intelligence 0.4546 -0.8970 1.0000 Random effects Note. The intercept corresponds to the (unweighted) grand mean; for each factor Figure 2